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What Is a Control Setup in Science?

The people in the control group act as a control set-up. They do not receive the factor or active medication that the people do in the experimental group, which acts as the experimental setup.

A controlled experiment can use a control group or a controlled setup, but is designed so that only one variable is manipulated at a time. This is necessary for the experiment to produce accurate results because if there are multiple variables then the scientists cannot know which variable produced which result.

The scientific method is used in the experimental process and in a controlled setup. The scientific method has several steps, which are: ask a question, do background research, construct a hypothesis, test the hypothesis by doing an experiment, analyze the data and draw a conclusion and communicate the results. The scientific method is the method by which all experiments are conducted and allows scientists to ask and answer scientific questions through observations and experiments.

Tackle Control Set-up Questions Like A Pro

Posted by Lim Zi Ai | Jul 14, 2016 | Experimental Techniques , Primary 5 Science , Primary 6 Science , Primary School Science Techniques | 0 |

Experiment-centric questions are increasingly common in primary school examination papers today.

There are typically six types of experiment-centric questions that can be tested:

Relationship – What is the relationship between X and Y?
Fair Test – How do we ensure a fair test?
Reliability – How can the experiment be more reliable?
Aim – What is the aim of the experiment?
Conclusion – What can you conclude from the experiment?
Control Set-up – What is the purpose of the control set-up?

I’m sure you’ve probably seen a variation of one of the above questions in your child’s examination paper.

Read Also Tackling Conclusion Type Questions FAQ: The Pique Lab – Experimental Techniques Masterclass The CUE Method To Tackle Data-Based Questions

Many students often struggle to structure their answers in a coherent & scientific manner, causing them to lose marks unnecessarily.

The good news is that we know what are the possible experiment-centric questions that can be tested!

We’re able to help your child prepare these answers ahead of their examinations & execute them when they see something similar. 🙂

Isn’t that amazing?

In our previous article, we discussed tackling conclusion-type questions , arguably one of the more challenging experiment-centric questions.

Today, we’ll be looking at how to tackle control set-up questions . It is often expressed in the following format:

What is the purpose of the control set-up?

For this particular question, students are often able to identify the changed variable and the measured variable in the question. The challenge lies in using the correctly identified variables to structure their answers properly.

As such, I would like to introduce a template answer to help your child with control set-up questions.

Thought Process

A control set-up is often needed to ensure that the experiment is valid & accurate. It serves as a baseline for the comparison of the results of the main experiment.

The control set-up will also rule out other environmental variables from affecting the results and eventually help to establish the conclusion of the experiment.

Ultimately, the control set-up is in place to ensure that the measured variable (dependent variable) is affected only by the changed variable (independent variable) and not any other variable in the experiment.

Template Answer

The purpose of preparing Set-up __ is to act as a control set-up to ensure that the _____________ (measured variable) is only caused by the ___________ (changed variable) and not any other variables in the experiment.

Final Answer

With reference to part (b) of the above question…

The purpose of preparing Set-up B is to act as a control set-up to ensure that the change observed in the limewater (if any) is only caused by the gas produced during decomposition and not any other variables in the experiment.

I hope that this article has shown you how to apply to correct template answer to easily tackle experiment-centric questions on control set-ups!

Over the past 13 years, Ms. Zi Ai has worked with primary and secondary students from a wide spectrum of backgrounds. She is highly proficient in helping her students navigate the thought processes and answering techniques required to excel in PSLE Science examination. As a Psychology graduate from the National University of Singapore, she actively seeks to use her knowledge to get her students to be intrinsically motivated to learn Science. Her secret lies in helping the child rediscover their interest for learning through her wide arsenal of humorous and inspiring teaching methods.

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Study guides for every class, that actually explain what's on your next test, experimental setup, from class:.

An experimental setup refers to the specific arrangement and conditions in which an experiment is conducted to investigate a hypothesis or research question. It involves manipulating independent variables, measuring dependent variables, and controlling extraneous factors.

Related terms

A control group is a group in an experiment that does not receive the treatment or manipulation being tested. It serves as a baseline for comparison with the experimental group.

The independent variable is the factor that researchers deliberately manipulate or change in an experiment to observe its effect on the dependent variable.

The dependent variable is the outcome or response that researchers measure or observe in an experiment. Its value depends on changes made to the independent variable.

" Experimental Setup " also found in:

Subjects ( 5 ).

Mathematical Crystallography
Plasma Medicine
Plasma-assisted Manufacturing
Statistical Mechanics
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Practice Questions ( 6 )

What experimental setup could be used to explore the impact of stress on academic performance?
If you want to examine whether neurogenesis affects memory retention, which experimental setup would be most effective?
Which experimental setup would effectively illustrate the concept of shaping in operant conditioning?
Which experimental setup would be most suitable for evaluating the effect of sensorimotor activities on cognitive development in toddlers?
What experimental setup could investigate the relationship between self-efficacy and goal-setting among college students?
Which experimental setup could effectively examine the role of peer pressure in altering teenage smoking attitudes?

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HOW TO TACKLE CONTROL SET-UP QUESTIONS

August 19, 2019 • Control Set-up • PSLE Science Answering Techniques

Control Set-up : Experimental-based Questions

Experimental-based questions are regularly tested in Primary School Science examinations. In this article I will share with you three question samples on how to tackle questions involving control set-ups by using " 3C " method ( C ontrol -C ompare -C onfirm ) .

(i) What is a Control Set-up?

An experimental set-up which is used for comparison in order to confirm the conclusion of an experiment.
An experimental set-up to show that the results obtained and observations made are only due to material tested and not other external factors.

(ii) Control Set-up Question is often expressed in the following format:

Variant 1: Why is there a need for set-up B?
Variant 2: Why is there a need for a control set-up?
Variant 3: Draw and label a control set-up for the experiment.

(iii) How to answer Variant 1, 2 and 3 Questions?

Variant 1 : It acts as a control set-up to compare and confirm that the (Measured variable / Dependent variable) is only due to the (Changed variable / Independent variable) and not due to other variables in the experiment.
Variant 2 : It is used t o compare and confirm that the (Measured variable / Dependent variable) is only due to the (Changed variable / Independent variable) and not due to other variables in the experiment.
Variant 3 : To draw the control set-up is to change/remove the (Changed variable / Independent variable) in the experimental set-up so as to prove that any change in result is only due to the (Changed variable / independent variable) and not because of other variables in the experiment.

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Open access
Published: 07 November 2024

3D-cell phantom-experimental setup to assess thermal effects and cell viability of lung tumor cells after electroporation

Noah Müller 1 ,
Severin Gylstorff 2 ,
Heike Walles 1 ,
Thomas Gerlach 3 ,
Othmar Belker 3 , 4 ,
Alessandro Zanasi 5 , 6 ,
Daniel Punzet 3 &
Sascha Kopp 1

Scientific Reports volume 14 , Article number: 27144 ( 2024 ) Cite this article

Metrics details

Experimental models of disease
Mechanical engineering
Regenerative medicine
Translational research

Medical devices and technologies must undergo extensive testing and validation before being certified for public healthcare use, especially in oncology where a high research focus is on new advancements. Human 3D-tissue models can offer valuable insights into cancer behavior and treatment efficacy. This study developed a cell phantom setup using a rattail collagen-based hydrogel to facilitate reproducible investigations into ablation techniques, focusing on electroporation (EP) for lung tumor cells. The temperature rise due to the treatment is a critical aspect based on other studies that have discovered non-neglectable temperature values. A realistic physiological, biological phantom is crucial for electrode material development, non-thermal ablation control, tumor cell behavior study, and image-guided treatment simulation. The test system comprises a standardized 3D-printed setup, a cell-mimicking hydrogel model cultivated with NIH3T3 and HCC-827 cell lines. The treatment is evaluated with an AlamarBlue assay and the temperature is monitored with a sensor and a non-invasive MR-thermometry. Results showed the reliability of the selected monitoring methods and especially the temperature monitoring displayed interesting insights. The thermal effect due to EP cannot be neglected and it has to be discussed if this technique is non-thermal. The lesions in the phantom were able to show apoptotic and necrotic regions. The EP further led to a change in viability. These results suggest that the phantom can mimic the response of soft tissue and is a useful tool for studying cellular response and damage caused by EP or other treatment techniques.

Introduction

Electroporation (EP) is a versatile minimal invasive technique utilized in different clinical and scientific fields like oncology, gene therapy, or microbiology and is described as non thermal 1 . The electroporation process involves the application of an external electric field with direct current (DC) to cells or tissues, creating micropores within the cell membrane. It can be used to introduce DNA molecules into cells, for electrochemotherapy due to the introduction of cytostatic drugs or as an ablation technique in the form of irreversible electroporation (IRE). The formation of pores in the cell membrane during electroporation occurs due to several mechanisms, including electrostatic charging of the lipid bilayer, rearrangement of the membrane proteins and mechanical stress on the membrane caused by the electric field 2 . These pores can be reversible or irreversible depending on the size 3 . Pores that do not reverse, initiate apoptosis in cells which is induced by the disruptive effect on cellular integrity and homeostasis. The process depends highly on the applied electric field, pulse parameters and the tissue of interest 1 , 2 . Studies evaluating the effect of different EP parameters have shown that the prediction of pore formation and cell viability based solely on given parameters (amplitude, duration, frequency and number of cycles) is unreliable, due to different tissue types and electric field distribution 4 . The applied voltage was shown to solely be a linear predictor.

As result, the need for biomaterial-based 3D tissue models, enabling the research on cellular responses to EP, is high. These models, following the 3R-principle (Refine-Reduce-Replace), are more time- and cost-effective, allowing for standardized high throughput analysis while representing very close proximity to human physiology 5 , 6 . Indeed, the Food and Drug Administration (FDA) stated that “it is important to recognize that considerably more research and development is needed for tools that might replace, reduce, or refine the large battery of animal studies [...]” 7 . To investigate EP, several in-vitro models are available. Biological in-vitro models have shown promising capabilities to investigate EP ablation efficacy and the evaluation of cell responses with different stainings regarding 8 , 9 . However, these models lack crucial aspects for a holistic evaluation like the applied temperature, the influence of electrode material and long-term cell responses. A systematic review by Hogenes et al. 4 showed that the quality of studies investigating the effect of electroporation was mainly low for 14 out of 18 studies (78 %). The most common are 2D and 3D cell cultures and vegetable models. Additionally, 3D spheroid models have been developed and utilized for studying EP. These models offer advantages such as a closer approximation to the tumor microenvironment and the ability to study cellular responses in a 3D context. However, they also have limitations, such as challenges in replicating larger tissue volumes and variations in spheroid size and shape, which can affect the consistency of results 10 .

Our model aims to address these limitations by enabling the study of larger volumes and offering more transferable results in certain aspects. Therefore, a standardized experimental setup combined with methods to monitor and evaluate is presented in this study to understand the effect of EP as well as the influence of specific parameters and treatment protocols.

Cell culture

Mouse fibroblasts NIH3T3 (American Type Cultur Collection, Virginia, USA, CRL-1658) were cultivated in DMEM high glucose (Sigma-Aldrich, Taufkirchen, Germany, D5796) supplemented with 10 % FCS (Bio&Sell, Nürnberg, Germany, FBS.S0615). Human adenocarcinoma cells HCC-827 purchased from the DSMZ (no. ACC 566) were cultivated in RPMI1640 (Thermo Fisher Scientific Inc., United States, 61870010) supplemented with 20 % FCS (Bio&Sell, Nürnberg, Germany, FBS.S0615). Both cell lines were cultivated under standard cell cultures conditions (37 °C, 5 % CO2).

Cell-laden phantom

Hydrogel models were composed of 2/3 rattail collagen-1 (10,5 mg/ml) (Fraunhofer-Institut, Germany) and 1/3 gel neutralization liquid (GNL) (Fraunhofer-Institut, Germany). In preparation of the cell-laden phantoms, 10 6 cells/ml were added into GNL before mixing both components over a 3-way-valve to initiate polymerization (Discofix C by B. Braun, Germany). Gels were cultivated in an insert (ThinCert by Greiner BioOne, Germany) placed into a 12-well plate (VWR, Belgium) and supplemented with cell-specific medium respectively.

Gelatine phantom

Gelatine phantoms were produced by mixing 29 mg/ml gelatine powder (Dr. Oetker, Germany), 10 mg/ml collagen powder (Vit4ever, Germany) and 30 % DMEM medium (Sigma-Aldrich, Taufkirchen, Germany, D5796) on a heating plate before it got poured into a container and placed at 4 °C.

3D printing

All 3D-printed parts were modelled using Fusion 360 (Autodesk Inc., United States, 2.0.18961), followed by exporting STL files and slicing using Ultimaker Cura 5.2.1 (Ultimaker B.V., Netherlands). An SV06 fused deposition modelling (FDM) 3d printer (Sovol 3D, China), facilitating a 0.4 mm nozzle was used to manufacture the parts from polypropylene filament (PP natur by Fiberlogy, Poland). The printing temperature was set at 235 °C and the bed temperature was 80 °C. Due to adhesion difficulties, the printer bed was coated with the Magigoo Pro PP (Thought3D Ltd, Malta). Sterilization took place at 121 °C in a LABOKLAV 135 MS autoclave (SHP Steriltechnik AG, Germany). The biocompatibility was tested following the guidelines for in vitro cytotoxicity and sample material defined in ISO-10993-5 and ISO-10993-12. Autoclaved PP cubes (6 mm x 6 mm x 3 mm) with the specific size and surface to achieve 3 cm 2 /mL were used to perform a direct and indirect test over 24 h. A MTT following a manufacturers protocol (Sigma-Aldrich, Taufkirchen, Germany, M2128-1G) was performed to measure the relative cell viability (RCV).

Electroporation devices

Two different devices were used to apply the electric pulses for electroporation. The Genedrive by IGEA SpA. (Carpi, Italy), which was specifically developed for preclinical studies and allows for a customization of the key electrical parameters (number, amplitude, frequency, duration and type of pulses) and an experimental MR-conditional EP device developed at STIMULATE campus 11 . The same plate electrodes were used with both devices (Customized Adjustable Plate Electrode, IGEA).

EP-procedure

Hydrogels were cultivated for 24 h before EP. The hydrogels were treated with electroporation buffer 12 (10 mM HEPES, 250 mM sucrose, 0.7 mM MgCl2, 0.3 mM CaCl2) to stabilize the electrical conductivity during the ablation. Gels were washed 3x times with PBS − followed by incubation for 10 min with 100 µl electroporation buffer. Both devices were set to deliver 8 rectangular pulses with 1000 V/cm, 160 µs pulse length and 25 cycles. The STIMULATE device had a pause time of 1000 ms between the cycles, while with the IGEA Genedrive, every cycle is started manually due to device safety. This results in a overall expanded treatment time with the IGEA device. Per cell line N = 8 hydrogels got treated while N = 8 untreated controls were performed. An AlamarBlue assay was done 24 h after the ablation. Histological sectioning was done 48 h after ablation (see section “Analyses”).

Temperature

The temperature was monitored during the treatment with a fiber optic temperature sensor (Optocon TS5/A366 by Weidemann Technologies, Germany) and a multi-channel thermometer (FOTEMP4-16 by Weidemann Technologies, Germany), which can be accessed via a laptop.

Oscilloscope

The STIMULATE device has no interface showing the delivered pulses. Visualisation was achieved using a Rigol DS1054Z Digital-Oscilloscope (Rigol Technologies, Germany) which monitors the delivered voltage and current of the pulses.

Proton resonance frequency shift thermometry

Proton Resonance Frequency Shift Thermometry (PRFS-Thermometry) is a non-invasive imaging technique that provides temperature change maps with high spatial resolution. PRFS-Thermometry relies on the resonance frequency of hydrogen atoms being approximately linearly dependent on temperature in molecules with hydrogen bond-bound hydrogen at temperatures around room temperature. By determining the phase difference between post- and pre-ablative in MR image phases, the temperature change can be calculated by the following mathematical model 13 :

\(\:\gamma\:\) - gyromagnetic ratio of hydrogen; \(\:\alpha\:\) - constant of correlation; \(\:{B}_{0}\) - magnetic field strength; \(\:TE\) - echo time.

Images were acquired on a 3T Siemens Magnetom Skyra using a Cartesian gradient recalled echo sequence (GRE) with a field of view (FoV) of 180 × 180 mm 2 , slice thickness 3 mm, resolution in plane 0.7 mm, TR 50 ms, TE 5 ms, flip angle 15 deg.

The baseline and post-ablative acquisition durations were approximately 1:17 min each (6 Averages). Inter-ablative thermometry was not feasible in the used setup due to electromagnetic interference.

Retrospective image analysis was performed using MATLAB [9.10.0 (R2021a)]. The acquired phase data was converted into radians, and temperature change maps were calculated using Eq. ( 1 ). A temperature offset was determined from a water bottle positioned at the edge of the FoV.

The AlamarBlue assay was performed following the standardized manufacturer protocol (Thermo Fisher Scientific Inc., United States, DAL1025). The incubation time was set to 20 h as a result of the standard curve. With respect to measuring the metabolic activity in the intended ablation area, located between the electrodes, a rectangular-shaped 3D-printed vacuum biopsy punch was used (See Fig. 1 C). Cell medium was used without adding FBS due to the possibility of mitigating the metabolism of the resazurin 14 . The fluorescence intensity was measured at 560 λ excitation and 590 λ emission using an Infinite 200 Pro microplate reader (Tecan Group Ltd., Switzerland). The percentage difference between treatment and control was calculated following Eq. ( 2 ):

Histological analysis

After treatment, samples were embedded in Histofix − 4 % formaldehyde (ITW Reagents, United States) overnight at 4 °C, followed by histological section cutting. Dehydration of samples was achieved using an ascending alcohol series (Isopropanol 20 − 100 %) for 24 h using an automated tissue processor (Leica Biosystems, Germany). Afterwards paraffin embedding was performed with a Histostar embedding station (Thermo Fisher Scientific Inc., United States). Precise cutting was executed using a rotary microtome HM355S (Thermo Fisher Scientific Inc., United States) with 10 μm slice thickness. The hematoxylin and eosin, as well as the alcian blue staining, are following standardized protocols 15 .

Experimental setup

The experimental setup for the EP follows a modular concept with several exchangeable 3D-printed parts (Fig. 1 ). All components can be sterilized and placed under a biosafety cabinet to achieve sterile working conditions. Figure 1 A shows the experimental setup, including the electrode mounting interface fixed onto a rail and the Bio-Phantom positioned underneath it. The electrode mounting interface (See Fig. 1 A-B) can be equipped with different electrode distance spacers and is modifiable to mount several electrodes or comparable ablation devices. Figure 1 B illustrates the cable duct which allows the fiber optic sensor to access the ablation area, while a positioning support ensures a parallel placement of the electrode with a fixed penetration depth. The insert, in concert with the hydrogel, is placed into a mount in the pocket of the healthy tissue phantom. The mold is created by placing a PP cylinder during the gelation process and removing it afterwards. While the cell-laden hydrogel measures a volume of 1 cm 3 , the healthy tissue phantom has a size of 60 cm 3 . The ablation device was precisely inserted through vertical movement on the rail into the phantom placed in cutout of the glass bottom. A rectangular vacuum biopsy punch, displayed in Fig. 1 C, was developed to separate the intended ablation area, while preventing the surrounding tissue affecting the metabolic assay.

A : Experimental setup for an EP procedure including the electrode mounting interface positioned on a rail and the Bio-Phantom; B : Cross-section of the electrode mounting interface including the positioning support, temperature sensor and plate electrodes, and the Bio-Phantom consisting of the tumor hydrogel, healthy tissue phantom C : 3D-printed biopsy punch for isolating the ablation area of a hydrogel. The printed part is connected with a luer-lock system to a 20 ml syringe.

The developed parts used in the setup, like the biopsy punch, have direct contact with cellular components, which makes the biocompatibility of the printing material necessary. The results and setup of the executed test is seen in Fig. 2 A-B. The direct test with the autoclaved PP cube for N = 6 shown in row one (Fig. 2 A) had a relative cell viability (RCV) of RCV direct = 79 %. The indirect test with cell media incubated for 24 h with PP cubes shown in row two (Fig. 2 A) had a RCV indirect = 115 %. The negative control is untreated and is set to be the reference for 100 % cell viability.

Experimental setup of biocompatibility test (ISO 10993-5) with 5 × 10 4 NIH3T3 cells per well. ( A ) Presents the MTT assay setup with direct contact of sterilized 3D-printed PP cubes in the first row. The indirect assay, in the second row, was executed with cell medium which was incubated for 24 h with sterilized 3D-printed cubes. Negative control is untreated, positive control was incubated with 0.05 % SDS and the blank stays empty with no cells seeded. The Well-plate was incubated for 24 h before measurements to allow cells to react to the treatments. ( B ) The relative cell viability (RCV) measured using the MTT assay is shown in the graph with the negative control set to 100 %.

Potential compatibility and reliability across diverse imaging applications allows versatile investigations and monitoring of the Bio-Phantom.

The MR image shown in Fig. 3 A was acquired using a 3T Siemens Magnetom Skyra with a T1-vibe sequence (field of view (FoV) of 96 × 96 mm 2 , resolution 1 × 1 × 1 mm 2 , TR: 7.17 ms, TE 2.93 ms, flip angle: 12°). It shows a slice of the Bio-Phantom in the sagittal plane, where the cell-laden collagen hydrogel is marked with a red arrow, while the healthy tissue phantom by a yellow arrow. Both portions are displayed as a homogenic mass. Small artefacts are visible in the surroundings of the hydrogel, where the insert is placed.

The CT image was acquired using a Siemens SOMATOM X.cite with the following parameters: Collimation, 38.4 × 0.6 mm; Voltage, 70 kV; Tube current 28 mA; Slice thickness 2 mm; Pitch factor 0.8. The raw data was reconstructed using a Br40 convolution kernel. Figure 3 B shows a slice of the Bio-Phantom in the transverse plane with the cell-laden hydrogel marked in red and the healthy tissue phantom in yellow, while the PP casing is visible on the outskirt. Cell-laden hydrogel as well as the surrounding matrix presented a homogenic mass.

Bio-Phantom consisting of healthy tissue phantom (yellow arrow) and tumor hydrogel (red arrows) captured with A : MR imaging (3T Siemens Magnetom Skyra with a T1-vibe sequence) and B : CT (Siemens SOMATOM X.cite).

Temperature monitoring

To examine the impact of an EP, it is essential to monitor the temperature rise to ensure that cells are not at risk of undergoing thermal necrosis.

The temperature monitoring with the fiber optic sensor in the center of the ablation area is exemplary shown for single measurement with 1000 V/cm in Fig. 4 displaying a temperature increase for both devices. The STIMULATE device led to a linear increase (R 2 = 0.98) of ~ 13 K over a time span of 0:27 min resulting in ~ 0.5 K/s (Fig. 4 A). While the IGEA Genedrive, with a longer treatment time of in total 2:35 min, led to an increase of ~ 8 °C with a linear coefficient of determination R 2 = 0.95. The graph in Fig. 4 B shows 25 peaks related to 25 induced pulse cycles.

In addition, non-invasive temperature monitoring was performed using MR PRFS-thermometry. Figure 4 A shows the result after treatment with the STIMULATE device determining a temperature increase of approximately 10–15 K in the area between the electrodes. The IGEA device is not compatible in the MR environment, thus hindering the performance of a MR thermometry. Noise is shown throughout the thermometry image and artefact are displayed especially in gelatine phantom and the area around of the hydrogel.

A : Temperature measured during treatment with the Stimulate IRE at E = 1000 V/cm with 25 cycles of 8 pulses (left) in the center of a hydrogel with a fiber optic temperature sensor (left) displaying ∆T = 14 K, ∆T/∆t ≈ 0.5 K/s, and PRFS-thermometry of Bio-Phantom(right) displaying ∆T ≈ 10–15 K in the ablation area. B : Temperature curve measured in the center of a hydrogel with a fiber optic temperature sensor during treatment with the IGEA GeneDrive at E = 1000 V/cm with 25 cycles of 8 pulses. The graph displays ∆T = 7.5 K ∆Tc ≈ 0.8 K/cycle.

All histological sections displayed were cut in the transverse plane of the hydrogel, separated by the rectangular biopsy punch.

The alcian blue staining (Fig. 5 A) of the control with NIH3T3 cells shows the cell distribution throughout the gel with a cell exemplary marked with a yellow circle. To investigate homogenic cell distribution the coefficient of variance of the mean near-neighbour distance (COV d ) was calculated with Eq. ( 3 ) 16

s d = standard deviation; d = mean nearest-neighbour distance.

In a study by Ayyar et al. 16 the particle distribution was categorized as follows: ordered distribution COV d = 0.09, random distribution COV d = 0.32 and clustered distribution COV d = 0.69. The histological section presented in Fig. 5 A has a calculated value of COV d = 0.49. Cell counting and distances were acquired using ImageJ.

In Fig. 5 B the HE staining of the control displays the cytoplasm with dark purple stained cells marked exemplary with a yellow circle.

The HE staining of the sample treated by the STIMULATE device with 1000 V/cm, 8 pulses and 25 cycles is shown in Fig. 5 C. In the area of the inserted electrode is, compared to the control in Fig. 5 B, a section with a depth of ~ 150 μm in purple. Fiber-like structures occur throughout this segment marked with a yellow arrow. Figure 5 D, E display the result of the fluorescence intensity measurements for the AlamarBlue assay of NIH3T3 and HCC-827 cells treated with the STIMULATE device. The metabolic activity of treated HCC-827 cells decreased by 44 % compared to the controls, whereas NIH3T3 cells exhibited a 49 % reduction. NIH3T3 cells have a higher standard deviation of σ C = ± 17 % and σ IRE = ± 15 %, while HCC-827 is at σ C = ± 2 % and σ IRE = ± 4 %. These results are significant with p < 0.0001 for 95 % confidence interval.

A-C Stainings of paraffin-embedded collagen-based hydrogel containing NIH3T3 cells (yellow circle). A : Alcian blue staining displaying the cell distribution throughout the section. B : HE staining of untreated control with homogenic staining of the ECM. C : HE staining of hydrogel treated with Stimulate IRE at E = 1000 V/cm with 25 cycles of 8 pulses, displaying a ~150µm wide purple-stained area at the insertion point of the plate electrode and fiber-like structures marked with a yellow arrow. D-E : Relative metabolic activity of [N=8] collagen-based hydrogels 24 hours after treatment with the Stimulate IRE at E = 1000 V/cm with 25 cycles of 8 pulses for D : Cultivated with 1 × 106 HCC-827 cells. E : Cultivated with 1 × 106 NIH3T3 cells. The reference were each N = 8 untreated hydrogels. Values are generated with an AlamarBlue assay with 20 hours of incubation time.

New medical devices and technologies have to be tested, optimized and validated before they can be certificated according to the medical device regulation (MDR) and transferred into the public health care system 18 . In-vitro models are less time consuming and less expensive compared to animal models, allowing rapid prototyping. These models can be used to validate the devices but also can be used for research purposes 17 . In this case, human 3D tissue models have wide application options to help understand cancerous behaviour and evaluate cancer treatments. The rapid evolution of medical technologies and devices, coupled with the growing shift towards alternatives to animal models, like the Directive 2010/63/EU of the European Parliament which was implemented as “an important step towards achieving the final goal of full replacement of procedures on live animals for scientific and educational purposes” 19 , is increasing the demand for suitable models. Essential for creating such models is the combination of a Bio-Phantom, reliable treatment procedures and readout methods.

When establishing an experimental setup for the evaluation of EP, the standardized treatment and the monitoring of thermal impacts is crucial to ensure cells do not undergo thermal necrosis 20 . The electrode mounting interface developed in this study combines these requirements by ensuring a reliable electrode placement while being able to adjust the electrode distance and having an access point for the temperature sensor. It also ensures a parallel positioning of the electrodes which is necessary for an adequate EP, because an uneven electric field distribution can cause an inconsistent exposure and possibly lead to varying treatment results 21 . In previous studies, when collagen-based hydrogels were facilitated, no needle guidance was implemented 9 .

The setup is mostly composed of PP which needs to be biocompatible due to direct cell contact. The biocompatibility was tested using an MTT test (Fig. 2 ) resulting in a RCV above 70 % for the direct as well as the indirect contact to the material. With accordance to ISO 10993-5 standards, the material can therefore be considered as non-cytotoxic.

Histologic sections (Fig. 5 A) of the hydrogel itself showed to have cell distribution in between a random and a clustered value. Clustering in 3D cell cultures is a typical phenomenon due to a non-uniform ECM 22 . The alcian blue staining (Fig. 5 A) further showed no optical cell polarization is present throughout the section indicating a successful nutrient supply throughout the gel 23 .

Imaging compatibility

Compatibility with imaging techniques allows for the utilization of non-invasive monitoring methods. In combination with devices that can be used in MR environments, like the STIMULATE device, the Bio-Phantom can be used for the training of image-guided interventions, followed by treatment analysis using multiple assays. Further imaging sequences can be optimized for specific ablation techniques. Another field of application is the Bio-Phantom as a training tool for physicians. A study from McHugh et al. 24 demonstrated the ability of a cell mimicking phantom and the use of Apparent Diffusion Coefficient (ADC) MR imaging to detect tumor tissue. Adding spheroids into the hydrogels like in a study from Kim et al. 25 could be used to train physicians to detect tumor sites.

The MR-image of the Bio-Phantom showed artifacts in the area around the hydrogel (Fig. 3 A), which can be explained as susceptibility artifact due to air enclosures 26 . The separation of the hydrogel and the gelatine phantom by the plastic insert is the reason for that. To improve the image quality and avoid artifacts, the Bio-Phantom should be cultured as one hydrogel containing a healthy tissue and a tumor site, which could be realized with a spheroid 27 . Upscaling the hydrogel would further enhance its image quality and the size also limits potential MR techniques like the thermometry shown in Fig. 3 A. The resolution is not ideal, due to parameters being optimized to detect temperature changes and not for anatomic images. A larger model would enable electrodes with more needles used for larger ablation area.

Electroporation is stated to be a non-thermal technique with the benefit of preserving the extracellular matrix (ECM), while creating pores in the cell membrane 1 . The associated temperature changes during the electroporation process remain a subject of debate and investigation 21 . The extent and implications of temperature rise during electroporation procedures can have profound effects on cell viability, membrane integrity, and overall treatment outcomes 21 . When using EP as an irreversible ablation technique for tumor cells, the aim is the disruption of the cell membrane integrity leading to apoptosis, rather than necrosis. Necrotic cell death leads to the release of inflammatory cellular contents and can, especially in tumor tissue, promote the effect of metastasis 28 .

The temperature rise of ~ 10–15 K associated to EP pulse protocols displayed by the temperature curves in Fig. 4 was comparable to other studies using tissue mimicking phantoms or simulations 4 , 9 , 29 . However, these studies included no cell-laden phantoms to correlate the thermal effect on cells and the ECM. Dewhirst et al. 30 showed that the thermal necrosis process has an exponential correlation between temperature and the exposure time with 43 °C as a breakpoint. At 43 °C cells exhibited signs of thermal necrosis after ~ 500 min, whereas at 55 °C necrosis was initiated after 30 s. These results demonstrate the importance of monitoring the temperature and the influence of EP parameters (Field strength, Electrodes distance, pulse numbers, pause time) as well as the different electrodes. A study by van den Bos et al. 21 demonstrated the correlation between temperature rise and increasing voltage, pulse length and electrodes distance. Comparing both devices used for this study the longer pause time between pulse intervals of the IGEA Genedrive showed the tissue to cool down ~ 0.5 K after every peak. This also showed that the developed experimental setup can help adjusting treatment planning to stay under the necrotic temperature thresholds. The H&E stainings in Fig. 5 B-C display at the insertion point of the electrode plate anomalies that have similarities to a burn wound, while the alcian blue staining eliminates the possibility of a dye error. During a study by Cannon et al. 31 H&E staining was performed to identify burn depth on the skin of a pig which presented comparable purple sections with an increasing area for longer burn time. The different dye gradients can occur due to collagen denaturation. Furthermore, in Fig. 5 B bundled collagen fibers (yellow arrow) are present, which is an indicator of a thermal influence 32 . Another study 21 revealed the incident of light flashes on the negative electrode during EP. Together with the rise of the temperature measured with the fiber optic sensor and the MR-thermometry a tissue burn can be assumed. Further assays, like the investigation of heat shock protein markers need to be performed to validate these findings on a cellular level.

It also seems feasible to simulate numerically. However, numerical simulations have inherent limitations, such as accurately modeling the complex biological responses and thermal effects observed in experiments. Correlating experimental results with simulations could provide deeper insights into the electroporation process, but it requires precise input parameters and validation against experimental data to ensure reliability and applicability. The integration of experimental and simulation approaches could enhance the understanding of EP effects, yet challenges in accurately replicating biological conditions and responses remain.

Cell viability test

Cell viability tests measure the overall health, activity, and functionality of a cell and values can be correlated to estimate the number of living cells 33 . The AlamarBlue assay used in this study is based on the reduction of resazurin to highly fluorescent resorufin by metabolic active cells and is published to be non-cytotoxic at the working concentration of 10 % 34 . A study by Bonnier et al. 35 covers a comparison of the use in 2D and 3D cell cultures. The result showed that the assay is reliable when prolonging the incubation time due to the diffusion time into the 3D matrix. In accordance, a suitable incubation time was measured at 20 h by creating a standard curve.

Other studies showed that the optimal timepoint for measuring the cell viability after an EP was 24 h post treatment 4 . This is due to the fact that cells can recover from certain membrane disruptions and there are differences in cell line-specific membrane repair mechanisms. A study 36 has shown that tumor cells have strong repair mechanisms against membrane disruption due to ESCRT processes (Endosomal sorting complexes required for transport). This would lead to the assumption that the viability of tumor cells after the EP should be higher. Results displayed in Fig. 5 C-D showed contrary values with HCC-827 cells having a lower viability then NIH3T3 cells. Tumor cells are in general less mechanical resistant and the induced mechanical stress due to the EP can be the consequence for that 37 .

The results of the AlamarBlue assay displayed in Fig. 5 D-C indicates a cell viability reduction after EP of under 50 %. Since the measured region was exclusively the ablation area, separated by a biopsy punch, a higher decrease of the cell viability was expected since the field strength of E = 1000 V/cm is stated to lead to irreversible cell damage 5 , 38 . The thermal effect due to EP cannot be neglected and it has to be discussed if EP is a non-thermal technique. Further investigations on heat stress markers are need to be performed to trace back the cell viability reduction onto the pore formation or thermal necrosis.

In conclusion, an experimental setup was developed to establish a reliable treatment process integrating a monitoring and analysis system to enhance investigations into the effects of EP on tissue. The thermal effect can be monitored with an invasive and non-invasive technique and the first investigations showed reproducible results. Further investigations on the biological process of thermal necrosis due to EP is possible. A combination of spheroids and hydrogels could give a more complex and realistic in-vitro model where the limitations of each model alone can be overcome. Considering the possible reversible effect of EP, the established assays enable long-term investigation. An important improvement would be the upscaling of the hydrogel to increase image quality and also enable more fields of application. The system has the potential to be adapted to other ablation techniques due to the modular concept. Further studies could investigate different ablation parameters or techniques and the cellular response to the thermal influences.

Data availability

Data is provided within the manuscript. Original data can be provided upon request by contacting the corresponding author.

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Acknowledgements

We would especially like to thank Joris Hubmann for his excellent assistance with methodological and organizational questions regarding the EP Stimulate device. We thank Marcel Gutberlet for his support in MR-Imaging. We also thank Thomas Rauff and Simona Salati of IGEA S.p.A. for their support and assistance in the use of the IGEA gene drive.

Open Access funding enabled and organized by Projekt DEAL. This work was financed by the German Federal Ministry of Education and Research within the Research Campus STIMULATE, grant no. 13GW0473A and 13GW0473B.

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NM, SK, SG; OB, TG: Formal analysis, Supervision, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. HW: Conceptualization, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing. AZ, DP: Methodology, Resources, Writing – original draft, Writing – review & editing.

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Müller, N., Gylstorff, S., Walles, H. et al. 3D-cell phantom-experimental setup to assess thermal effects and cell viability of lung tumor cells after electroporation . Sci Rep 14 , 27144 (2024). https://doi.org/10.1038/s41598-024-78339-w

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Introduction: Practices, Strategies, and Methodologies of Experimental Control in Historical Perspective

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First Online: 27 February 2024

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Jutta Schickore 13

Part of the book series: Archimedes ((ARIM,volume 71))

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The introduction distinguishes four distinct strands in the history of experimental control. The first is the historical development of control practices to stabilize and standardize experimental conditions. The second is the emergence and career of the comparative design in experimentation, understood as a way of generating and securing knowledge of cause-effect relations. The third involves the unfolding, both in philosophy of science and in the sciences themselves, of methodological discussions on control practices and designs in experimental practice. The fourth is the history of the term “(experimental) control.” The introduction describes how the contributions to this volume address these aspects of experimental control.

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Control is the hallmark of scientific experimentation. If an experiment is deemed to be lacking in control, it is unlikely to gain traction in the scientific community; arguably, an uncontrolled intervention is not even a genuine experiment. Today, scientific articles routinely mention controls and handbooks and instruction manuals on methods in the life sciences call for controlled experiments. Evaluating the appropriateness of controls is a core element of successful peer-review.

But despite its centrality to modern scientific inquiry, many foundational and historical questions about experimental control remain open. Experimental practice has been studied for decades, but only few analyses of scientific control practices in experimentation exist, Footnote 1 with almost nothing written on controlled experimentation in the longue durée . Footnote 2 We know little about changing expectations for well-controlled experiments or about different kinds of control, experimenters’ interpretations of control, or reasons given for applying controls. There is not even consensus about whether experimental control is an ancient, early modern, or Enlightenment concept, or whether it is a more recent feature of scientific inquiry. Footnote 3 This is, in part, because the concepts “control,” “control experiment,” and “controlled experiment” are polysemous, like “replication” or “significance.” In addition, methodological concepts for experimental practice have until recently received comparatively little scholarly attention.

“Control” has been studied mostly as a broader cultural phenomenon in the Western world. Cultural histories of control focus on ideologies and technologies for governing people, procedures, or systems of machines (Levin 2000b ; Derksen 2017 ). Historical studies of control and science have shown how cultural currents, for better or worse, transformed scientific practices into more rigorous endeavors. Historians of science have noted the increasing importance in science of a quantifying spirit (Frängsmyr et al. 1990 ) and the values of precision (Wise 1995 ). They have examined the influence on science of tools such as statistics (Porter 1995 ; Gigerenzer et al. 1989 ) and surveillance devices (Foucault 1975 , 1979 ), as well as bureaucratic procedures such as record-keeping, double bookkeeping, and accounting. These authors have argued that institutional changes in science, such as the rise of the university and urban research laboratories, have helped to standardize scientific practice and make it more exact (Tuchman 1993 ; Dierig 2006 ). Eighteenth-century sciences of state promoted record-keeping, accounting, and statistical assessments of experimental data (Seppel and Tribe 2017 ). Nineteenth- and twentieth-century physics and engineering helped to create automated feedback control mechanisms (Bennett 1993 ), intertwined control and communication systems (Wiener 1948 ), and “networks of power” (Hughes 1983 ). They also brought about catastrophic failure of control, as in failed aerospace missions, plane crashes, and collapsing bridges (Schlager 1995 ). Industrial and technological advancements allowed researchers to engineer the development of living organisms and human heredity (Pauly 1987 ; Paul 1995 ), to standardize living things as model organisms for experiments (Rader 2004 ), and to measure human performance (Rabinbach 1990 ). The twentieth-century nexus of military, industry, and information technologies enabled wide-ranging control over data and information flow (Galison 2010 ; Franklin 2015 ).

Of course, broader socio-political and cultural developments such as industrialization, the institutionalization of university research laboratories, and the expansion of bureaucracies and state administration are impactful. These developments change how practices of research, recording, and record-keeping are organized, as so many authors have demonstrated. But they do not fully determine experimental designs or experimenters’ views on what is considered good and well-controlled or deficient and poorly controlled experimental practice.

This volume shifts the focus from broader socio-political and cultural contexts of control onto practitioners’ methodological strategies of inquiry and experimental design. While acknowledging that broader cultural forces do affect control practices, we contend that these forces only partially shape experimental design and strategy. We identify additional social dimensions of experimental control. On the one hand, identifying experimental conditions, confounders, and solutions to technical problems in experimental design takes time, and unfolds by the activities of multiple individuals or groups. On the other hand, whether an experiment counts as “sufficiently” or even “fully” controlled is not entirely decided by the experimenters themselves, nor can the question be settled by comparing actual experimentation with an abstract standard of the ideal controlled experiment. Footnote 4 The adequacy of control critically depends on the social interactions and negotiations among experimenters and their various interlocutors; as such, the issue is open to revisiting, revision, and renegotiation.

To capture the complicated and multilayered history of experimental control, it is useful to distinguish control strategies, control practices, and methodological ideas about experimental control. Control strategies are general designs and plans to follow in an experiment, like the comparison of an intervention target with a control. Control practices are the concrete actions by which experimenters implement control strategies in particular contexts. These contexts comprise all the resources available to the experimenters, including materials, tools, techniques, local expertise, and institutional opportunities. Methodological ideas are the broader notions of how to study nature and everything in it. They are contained in accounts of control strategies and practices, as the practitioners themselves give them. Footnote 5

Contributions to this volume deal with the details of experimental control practices, as well as with the expectations and perceived obstacles for experimental designs. The chapters are also sensitive to long-term developments of control strategies and methodological ideas. We provide a set of focused studies on control practices, strategies, and ideas that, together, cover a period of more than 300 years, with glimpses back to antiquity and forward to the late twentieth century. We contend that the long-term perspective is productive for understanding experimental methodologies and experimental control in particular. Footnote 6 The chapters offer several examples of how control practices using those strategies and ideas are shaped by local contexts—material-technical, conceptual, and social. Together, they illustrate that control strategies and methodological ideas often remain stable for a long time and change only gradually.

To study controlled experimentation from a historical perspective, we must distinguish at least two notions of control. The first is a broad sense of control as “managing,” “restraining,” or “keeping everything stable except the target system to be intervened upon.” This notion primarily but not exclusively concerns the experiment’s material side—the objects, the setting and environment, and the tools, as well as the guided manipulation or intentional intervention in an otherwise stable situation to see what will happen. Footnote 7

In an uncontrolled situation, experimenters cannot determine the changes resulting from their interventions. To extract information from unwieldy experimental situations, they must standardize instruments and experimental targets and hold fixed the experimental background conditions. They ought also to be free of preconceived opinions and other sources of influence. Experimenters seek to make the experimental setting and background as stable and rigorous as possible because effects, both expected and novel, appear most distinctly against a stable background. Footnote 8 Generally, then, we can consider any aspect of experimental practice from the perspective of control; a key question is how experimenters identify what must be controlled in concrete contexts and how they achieve that control.

There is also a narrower notion of control, referring to comparative experimental designs. Footnote 9 It primarily but not exclusively concerns the experiment’s epistemic side, or the conditions required for the experiment to generate knowledge. Modern scientists typically associate with “control experiment” a particular experimental strategy or design, namely the comparison to a control case. An experimental intervention is compared with a baseline; the target system of the intervention is compared with a similar target system that, unlike the experimental object, was not intervened on (the “control mouse,” say, which did not receive treatment). This strategy encapsulates the requirements for an experiment to be informative about cause-effect relations. Footnote 10

In the narrow sense, comparison to a baseline is needed to find out whether it really was the manipulation of this particular variable that made a difference to the experimental outcome. Footnote 11 Of course, the more similar the experimental situations are, the more informative the comparisons will be. Making informative comparisons thus requires control practices in the broader sense explained above, to ensure that the two experimental settings are stable, save the intervention.

We should avoid confusing the emergence of terms such as “control experiment” and “experimental control” in the scientific literature with the emergence of explicit discussions about control practices and strategies. The terms “control experiment,” “controlled experiment,” and “experimental control” are recent terms. Google Ngram shows a steep increase for “control experiment” in the last decades of the nineteenth century in English, French, and German-language scientific literature. Of course, Ngram is not a rigorous tracker for word usage, but based on its data, we can safely assume that control practices were common long before the term spread in scientific writing. Footnote 12 As our volume demonstrates, discussions about stable experiments antedate the appearance of the term “control” in this literature. Concerns about the adequate management of experimental settings were voiced as soon as experimentation became widespread. Robert Boyle, for one, published two famous essays on “unsucceeding” experiments, where he discussed the obstacles posed by impure chemicals, the variability of body parts in different corpses, and other issues threatening experimental success (Boyle 1999a , b ).

The history of experimental control, then, encompasses four distinct yet related strands. The first is the historical development of control practices to stabilize and standardize experimental conditions. The second is the emergence and career of the comparative design in experimentation, understood as a way of generating and securing knowledge of cause-effect relations. The third involves the unfolding, both in philosophy of science and in the sciences themselves, of methodological discussions on control practices and designs in experimental practice. The fourth is the history of the term “(experimental) control.”

This volume concerns itself most with the first three strands. We do not systematically explore the history of the term “control;” Footnote 13 in fact, several contributions discuss research from before the late nineteenth century. However, precisely because control practices and strategies predate the term “control” in scientific literature, we keep terminological questions in mind as we analyze past experimental reports and methodological discussions. We pay careful attention to the terms past practitioners did use, whatever they were, to describe, explain, and defend control practices and strategies.

The contributions here examine how control practices and comparative designs developed, and include past accounts of critiques and defenses for these practices. Control is a multifaceted and elusive concept, and our volume reflects this. We have not attempted to reduce our discussion to a single definition of “control.” Although this introduction provides some points of orientation for analyzing control practices and strategies, each contributor further explains the concept for specific experimental contexts. The chapters range over different fields, from botany and vision studies, ecology and plant physiology, human physiology and psychology to animal behavior and experimental physics. They cover a period from the early seventeenth to the twentieth century. They examine experiments with complex and sometimes unwieldy objects and elusive phenomena. Chapters deal with studies on learning and judgment; color blindness in animals; auditory perceptions of tones, pitch, and vowel sounds; irregular movements; psychic forces; unobservable elements; and the best “photogenic climate” for promoting photosynthesis. Experiments on such objects and phenomena are hard to design, stabilize, and carry out, and they are often controversial. For this reason, they showcase questions and reflections on control in science particularly well.

The very practice of creating and maintaining a stable experimental situation is old, arguably as old as experimental intervention itself. Over time, experimenters learn what must be managed and tracked in experimental contexts; they seek to localize the phenomena of interest as well as the elements of the experimental setting in order to make interventions more exact. Gradually they develop new tools to do this. Precision instruments, elaborate recording devices, and other technologies available in the last century or two can assist with these tasks. The history of research laboratories can be written as the history of efforts to create highly controlled research environments. Nineteenth-century physicists worked at night or retreated to the lab basement to escape city noise, vibrations from trams, and exuberant students (Hoffmann 2001 ). Today’s scientists turn to specialized construction companies when they need “clean rooms” for research. Footnote 14 All-metal or all-plastic labs are built for research into the impacts of micro-plastics on materials and tissues or on radiation, respectively. Particle physicists dive to recover radiation-free lead from ancient shipwrecks to prevent contaminating their measurements.

Such materials and technologies often make it easier to keep an experimental situation stable and to track interesting changes. Footnote 15 At the same time, however, closer analysis of actual episodes shows that advancements in instrumentation, impressive as they may appear in hindsight, do not guarantee improved control. In fact, obtaining control often becomes more difficult, not least because researchers must learn the instruments’ proper functioning. “The more finely a method of investigation operates, the more complicated the devices used must be,” as Carl Stumpf noted (1926, 8). Footnote 16

Moreover, the history of control is a history of efforts—and efforts can fail. Implementing control strategies often fails, as even the experimenters themselves sometimes admit. Our volume illustrates how difficult it can be to manage an experimental setting, how resourceful some experimenters were in their management, and how they sometimes failed to achieve it despite intense effort. Claudia Cristalli’s researchers of psychic phenomena walk the line between controlling the psychic powers of the “percipients” in their experiments, and preventing them from sensing any phantasms at all. Christoph Hoffmann’s study of color blindness in fish shows how experimenters dealt with the tricky problem of controlling animals’ behavior. Experimenters found different solutions, both difficult to implement and neither completely satisfying. One option was to train the fish—much more challenging to do than training, say, a dog or rat. The other was to design the experimental setting in such a way that the “normal” behavior of the fish was taken into account when the behavior of interest was elicited. But what is the “normal” behavior of fish? And how can it be accommodated in the unnatural environment of a laboratory fish tank?

Other contributions illustrate how experimenters approached the creation and monitoring of an experimental setting. They discuss the multifaceted nature of the associated problems and the obstacles the experimenters had to overcome when attempting to stabilize unwieldy things, such as the irregular movements of microscopic parts, the germination, sprouting, and growth of plants, and auditory perceptions. The contributions describe the solutions they found to these problems. Experimenters tried their best to identify the smallest details of the experimental settings deemed relevant, and sometimes invented remarkably elaborate contraptions to keep them stable.

Caterina Schürch depicts the curious machines with which eighteenth-century plant physiologists tried to electrify plants and seeds with precise doses of electricity. Kärin Nickelsen shows how the nineteenth-century plant physiologist Julius Wiesner designed an artificial environment for his plants: double-walled glass jars, with the space between the walls filled with a solution of iodine in carbon disulphide. Because this liquid layer absorbed all visible light but heat rays, Wiesner could examine the impact of those rays on plant growth. Julia Kursell describes the giant arrangement of tubes Carl Stumpf erected to compare how his experimental subjects perceived natural and machine-generated vowels. She notes that, according to Stumpf, the increased finesse of experimental tasks required ever more complex experimental devices. Cristalli shows how Faraday, attempting to stop participants in table-turning experiments from making involuntary movements, designed a device consisting of a stack of cardboard sheets, arranged like a voltaic pile, with pellets of wax in between. The device would be placed between the hands of the séance participants and the tabletop. The sheets were arranged and marked in such a way that their displacement would indicate hand movements prior to the table’s movement.

These devices often astonish with their ingenuity, but the point is that they are the material realizations of what experimenters recognized as the relevant conditions and potential confounders for their experiments. They are therefore purpose-dependent, as Kursell notes; at the same time, they both constitute and constrain the generation of experimental knowledge. Cristalli’s, Schürch’s, Nickelsen’s and Evan Arnet’s chapters demonstrate this constraint: over time, views about what factors to manipulate, keep fixed, or monitor in controlled experiments might change considerably, even within a single research tradition. While Faraday built tools to control his subjects’ involuntary movements, his American colleague and erstwhile admirer Robert Hare turned to designing machines that would prevent voluntary movements in psychic experiments—in other words, to prevent fraud.

Schürch’s account illustrates a most dramatic change of focus. After decades of carefully controlled experimentation, which supported the view that electrification promotes plant growth, Jean Ingen-Houz showed, using the same control strategies, that it was not electricity but differences in light intensities that affected the plants. He thus re-oriented the entire research program of plant growth, rendering previously “well-controlled” experiments uncontrolled.

Similarly, in maze research on animal learning, later investigators critiqued their predecessors for stabilizing—“controlling for”—the very phenomenon they should have studied, as Arnet’s work illustrates. Nickelsen shows how control practices in photosynthesis research changed fundamentally as the experiments moved from the laboratory to the field. As she observes, the changes were not just practical—measuring natural light is harder than measuring laboratory light—but also conceptual. What mattered was no longer just “daylight,” but a complex set of factors consisting of the specific light individual plant parts received, intensity fluctuations during the day and the season, and so forth. Klodian Coko charts another kind of reorientation in his study of research on Brownian movement. Using the strategy of comparative experimentation, nineteenth-century researchers tried to establish what could and could not be the cause of Brownian movement. Later in the century, Brownian movement itself became evidence for a new kinematic-molecular theory of matter, which changed the understanding of rigor and experimentation.

Several chapters also direct attention to the fact that many experimenters were explicitly concerned with developing coping strategies for “limited beings” (Wimsatt 2007 ) in sub-optimal situations. Researchers faced challenges not only because background factors were difficult or too numerous to monitor, but also because those factors were not immediately observable. Remarkably, the physicist Lord Rayleigh devoted several of his public-facing remarks to the theme of “deficient rigor.” As Vasiliki Christopoulou and Theodore Arabatzis point out, for Rayleigh, the pursuit of absolute (“mathematical”) rigor could even be detrimental to progress in physics. It was in this situation that experimenters insisted on using two or more different experimental techniques to check if both converged on the same outcomes, as detailed in the contributions by Christopoulou and Arabatzis and by Coko.

Notably, experimenters developed strategies to guard against entirely unknown influences on their experiments. The notion that natural phenomena in an experiment might occur and not occur in unforeseeable ways is centuries old. The metaphysical interpretation of this notion has changed dramatically over time (Hacking 1984 , 1990 ), but there was wide and long-standing agreement about how to address it: namely, through multiple repetitions of experimental trials. Both the early seventeenth-century experimenter Scheiner and the late nineteenth-century experimenter Rayleigh gave the idea of multiple repetitions an important role in rigorous experimentation, if for different reasons.

In an early essay on medical experience, the ancient physician and anatomist Galen discussed the possibility that what is seen only once in a patient may not be a regular occurrence, and thus may not be worthy of acceptance and belief. Galen suggested this point in the middle of his attempt to demonstrate that medical practice is not just logos , but also experience. Footnote 17 As part of the argument, Galen alluded to the instability of memory and also noted that medicines work sometimes but not always (Galen 1944 ). In clinical medicine, at least, one single drug test might not produce reliable results, because “some things are frequent and some are rare” (Galen 1944 , 113). It must therefore be repeated several times, and even then, it may not tell us what is usually the case. Footnote 18 Ibn Sīnā (Avicenna) expressed a similar idea in a proposal for rules of drug testing, albeit with a positive spin. He wrote that “the effect of the drug should be the same in all cases or, at least, in most. If that is not the case, the effect is then accidental, because things that occur naturally are always or mostly consistent” (Nasser et al. 2009 , 80).

In the early modern period, we encounter this idea frequently, now also in discussions about experimentation beyond drug testing in clinical medicine. Repeating experimental trials several times, indeed “very many times,” became an imperative for rigorous experimentation—in this way, unknown or contingent and accidental influences on experiments could be avoided. Footnote 19 In later centuries it was to become a hallmark of rigorous experimentation that a trial be done more than once or on large samples. Footnote 20 However, as Schürch’s chapter shows, the appropriate number of repetitions remained contested.

Scholars looking for the “first” control experiment in the history of scientific inquiry typically assume, but in most cases tacitly, the narrower notion of “control” as comparative trial. They have found quite early examples for comparative designs in experimental practice. These examples often come from medicine, where it is both vitally and commercially important to discover the efficacy of certain drugs and treatments. The reputation of a practitioner depended on the treatments’ success.

For example, historian of statistics Stephen Stigler finds an instance of comparative experimentation in the Old Testament, in the Book of Daniel (around 164 BCE). Servants on a vegetarian diet are compared with children who eat “the king’s meat”: “And at the end of ten days their countenances appeared fairer and fatter in flesh than all the children which did eat the portion of the king’s meat” (Daniel 1:5–16). Footnote 21

A passage by Athenaeus (200 CE) describes how some convicted criminals had been thrown among asps and survived. It turned out that they had been given lemons prior to their punishment. The next day a piece of lemon was given to one convict but not to another. The one who ate the lemon survived the bites, the other died instantly. Footnote 22 The pseudo-Galenic treatise on theriac describes a trial with a similar design, whereby two birds would be poisoned and only one given an antidote (Leigh 2013 ). The trial tests the efficacy of medicines: if both animals survived, the tested antidote was recognized to be ineffectual. That experiment was again reported in the Middle Ages, notably by Bernard Gordon (McVaugh 2009 ).

Another famous ancient example is the legend of Pythagoras. As the story goes, he observed that most combinations of blacksmiths’ hammers generated a harmonious sound when striking anvils at the same time, while some did not. Pythagoras discovered that harmonious sounds were produced by those hammers whose masses were simple ratios of each other, while other hammers made dissonant noises when struck simultaneously. Notably, Ptolemy later criticized the Pythagorean experiment because, to him, it lacked control (Zhmud 2012 , 307).

The Pythagorean case is interesting. It clearly has a comparative component, inspecting the sound of hammers whose masses were simple ratios of each other and that of other hammers. But in the historiography of science it does not serve as an example of an early “control experiment.” In fact, the ancient texts have too little information to determine whether it was consciously performed as an experiment compared with a control, whether Pythagoras simply varied the setup, or whether he arrived at his conclusions by observing different blacksmiths at work.

Conscious and explicit implementation of comparative designs appears to become more common in seventeenth- and eighteenth-century experimental practice. In his studies on the generation of insects, Francesco Redi famously compared samples of organic materials—“a snake, some fish, some eels of the Arno, and a slice of milk-fed veal in four large, wide-mouthed flasks” (Redi 1909 , 33)—kept in open and closed containers. The samples were periodically inspected for traces of life. No life developed in closed containers, which Redi took as evidence against the spontaneous generation of maggots from putrefying flesh. Here, the comparative design demonstrates a cause-effect relation through the comparison with a “control.” Redi showed that maggots in open containers were generated by flies’ eggs. Footnote 23

The case of spontaneous generation research illustrates particularly well why it is useful to distinguish between comparative design strategies and a broader notion of control as management of the experimental setting. Redi’s experimental research was not decisive, and after him many other experimenters investigated spontaneous generation. They all contested each other’s experiments and many argued that their opponents had not properly maintained the experimental settings; they also argued that they themselves really had taken the necessary precautions to do so. John T. Needham, for instance, claimed that he could demonstrate the spontaneous generation of animalcules in infusions. He told his readers that he had “neglected no Precaution, even as far as to heat violently in hot Allies the Body of the Phial; that if any thing existed, even in that little Portion of Air which filled up the Neck, it might be destroy’d, and lose its productive Faculty” (Needham 1748 , 638). Notably, he did not report a comparison with a vial that had not been heated in fire. It may have been superfluous to him, because it was obvious that animalcules would appear in it, as so often had been observed. The debates continued throughout the nineteenth century. Experimental designs and interpretations for possible contaminants varied, but the comparative strategy generally remained the strategy of choice. Footnote 24 As Schürch’s contribution shows, in the decades around 1800, experimenters across Western Europe advocated comparative experimental designs.

Reports of comparative trials can be found in many fields, from agriculture to clinical medicine. Footnote 25 A notable but little-studied example is steeping experiments (Pastorino 2022 ). A comparative experiment by Francis Bacon served as a template for many subsequent experiments on the effects of plant growth when steeping seeds in various fluids.

Our volume illustrates comparative trial designs in plant physiology, physics, animal behavior studies, and psychology. The episodes exemplify both the conscientious application of these strategies and the obstacles experimenters faced as they attempted to realize well-controlled comparative trials.

The earliest pre-modern reports of experimental trials and comparative designs contain little express discussion on control practices and strategies. There are exceptions, of course, especially in medical contexts. I already noted Galen’s writings, and we know that medieval scholars such as Ibn Sīnā developed rules for drug testing (Crombie 1952). Mostly, however, comparative designs were simply described and rarely justified; there was little explicit concern with managing the details of experimental settings. When ancient and medieval authors noted the drug test on two birds, they surely meant to show a test to support the drug’s efficacy, but the argument for the comparative approach often remained implicit. In modern scientific writing, by contrast, we sometimes find detailed discussions and justifications of experimental designs—in controversies about experimental results, in debates about the status of heterodox scientific fields such as research on psychic phenomena, and in situations of uncertainty.

In this volume, Tawrin Baker’s chapter on Scheiner and Christopoulou and Arabatzis’s chapter on Rayleigh epitomize both the scarcity and the abundance of practitioners’ discourse on their control practices and strategies. Scheiner demonstrated to his readers how experimentation could serve as a legitimate check on a theory of vision. He did not expound or defend methodological ideas in detail, although he did focus attention on the process of experimentation. Words and pictures conveyed the experimental setups. Scheiner instructed his readers to make certain experiences and experiments; he discussed the implications for the theory of vision. However, as Baker notes, several issues remained open, such as how often an experiment should be repeated or how one ought to deal with discrepancies. Christopoulou and Arabatzis’s chapter on Rayleigh shows that late-nineteenth-century scientists wrote not only about the details of their experiments but also about experimental control. Experimenters drew attention to how they had re-designed instruments to make their measurements more precise and how they had employed additional instruments to check the quality of their measurements. They often insisted on using two measurement methods to guard against error.

We still know little about the unfolding of methodological discussions in the centuries after Scheiner’s appeal to a variety of experiences and experiments and Boyle’s musings on unwieldy, “uncontrolled” experimental settings and about the practices appropriate for managing and extracting knowledge from these settings. Little is known about the emergence of explicit methodologies for comparative trials. According to some scholars, notably Edwin Boring, it was not until the mid-nineteenth century that we find such explicit methodologies. Boring associated the first methodology of comparative experimental designs with a philosophical text, John Stuart Mill’s System of Logic (Boring 1954 ) . While the contributions to our volume do not tell a comprehensive history of methodological accounts on experimental control, they do suggest that it would be misleading to identify Mill as the sole originator and principal representative of these accounts. Footnote 26 As Schürch’s, Coko’s and Nickelsen’s chapters demonstrate, Mill was one of several early-nineteenth-century commentators on science who urged investigators to keep background conditions constant across trials, to “analyze” the background into different experimental conditions, and to compare the effects of interventions in one setting to another setting left untouched. But a broader history of these developments would still be desirable.

Our volume also shows that reflections about and justifications of control strategies predate modern philosophies of science. From Schürch’s study of late-eighteenth-century plant physiology we learn that, prior to Mill, practitioners not only called for rigorous and properly managed interventions, but also did much more: they reflected on control practices as validation procedures and debated their relative merits, practicality, and limitations. They observed that, to be instructive, comparisons must be made on sufficiently similar experimental subjects in similar situations. At times they disagreed about whether they or their colleagues had done enough to control their experiments. They criticized each other for not making comparative trials, for not controlling the right thing, or for not repeating a trial often enough.

The content of these debates and reflections tells us something about the experimenters’ own understanding of methodological issues concerning control, rigor, reliability, certainty, and failure in experimentation. Christopoulou and Arabatzis’s and Coko’s chapters illustrate this. As many contributors show, satisfactory control of an experiment is, in the end, an intersubjective, iterative achievement. Schürch and Christopoulou and Arabatzis note that experimenters such as Ingen-Housz and Rayleigh call upon others to check the results they themselves had obtained and to contribute additional experiments. Footnote 27 Cristalli charts the decades-long negotiations and re-negotiations among physicists, chemists, and psychologists on experimental practices deemed adequate to study psychic phenomena. The experimenters understood that their projects’ success depended on “controlling” their interlocutors as well. Footnote 28

This volume does not aim to replace earlier systematic discussions in history and philosophy of science on these issues, such as those on epistemological strategies of experimentation (Allan Franklin), tests for error (Deborah Mayo), representing and intervening (Ian Hacking), and how experiments end (Peter Galison). Our volume complements them. In fact, our discussions overlap with these approaches as we trace the history of controls while keeping epistemological strategies of experimentation in mind. We do contend that re-directing attention to control practices, control strategies, and practitioners’ accounts thereof illuminates new aspects of the history of experimental practices.

Control strategies and practices can be viewed as long-term and short-term methodological commitments, along the lines suggested by Peter Galison ( 1987 ). Arnet’s contribution to this volume uses this approach. Material and conceptual organizations of experiments vary, as do the identification of target systems, conditions, and confounders. The tools for stabilizing them change as well and are often (but by no means always!) local, context-specific, and relatively short-lived. Modern technologies allow for creative and sometimes intricate solutions to the problems of stabilization, standardization, and tracking. Yet the strategies have long been in place.

Control strategies are persistent. Even in the most complicated settings and with the most elusive phenomena, experimenters try to implement established control strategies as best they can, as shown in Schürch’s study of plant electrification, Coko’s discussion of experiments on Brownian movement, Cristalli’s study of psychic experiments, Kursell’s work on elusive auditory judgments, and Nickelsen’s discussion of plant physiology. Experimenters look for experimental conditions and confounding factors; they vary them to weigh their influence on experimental processes; they probe for error (Mayo 1996 ); they make their interventions less “fat-handed” (Woodward 2008 ); they compare situations meant to be similar and assess robustness, presupposing the no-miracle argument (Hacking 1985 ). At the same time, they develop specific, contextual implementations for these strategies, and they do not always agree on whether a particular implementation is effective.

In doing all this, experimenters face both technical and conceptual challenges. It may take a long time to harness experimental conditions, identify potential confounders, and find suitable techniques for doing so. Solutions to control problems will typically remain less than ideal. Hoffmann’s contribution demonstrates this fragility in control procedures. In debates about spontaneous generation, it took centuries to refine the tools to prevent contaminations from reaching the materials under investigation, and every new tool generated new issues for further exploration. Along the way, the understanding changed regarding the causes, conditions, and potential modifying factors and confounders. New technical challenges arose as a result.

Several chapters show that the implementation of control strategies may generate entirely new technical and conceptual problems for the experimenter, or even produce “surplus findings,” as Kursell writes. Footnote 29 Nickelsen, for instance, tracks changes in both the conceptualization and the logistics of managing background conditions for experiments on the influence of light on plant growth. Christopoulou and Arabatzis suggest that disturbances in physics experiments could become research topics in their own right. Arnet’s work also brings into relief the problematic implications of an over-emphasis on rigor and control. Early mazes were designed as simple systems of tracks in order to minimize environmental cues. But for a more complete understanding of animal learning, later researchers re-introduced precisely those same environmental features. The early mazes embodied a regime of control that stripped animals of certain sensory and environmental cues. Those mazes, however, excluded exactly those features that later researchers thought essential to advanced rodent learning. Footnote 30

Finally, several chapters suggest that it is fruitful to think of experiments as “controls of inferences,” because this perspective also brings out relevant methodological issues and their historical development. As Baker demonstrates, for early modern experimenters coming to grips with their Aristotelian heritage, the role of experiments in scientific inquiry was a crucial issue. In hindsight, studying how they managed this issue can also tell us something about Aristotle’s own ideas on the role of experimentation in empirical inquiry. For eighteenth- and nineteenth-century inquirers, then, the question is not so much whether but how, exactly, experimentation and experimentally generated knowledge can help us to understand nature. Steinle, Coko, Nickelsen, Kursell, and Hoffmann show how intricate the question can be as experiments target unobservable phenomena. As these experiments involve increasingly complicated instruments, hypotheses, assumptions, chains of inferences, and interpretations, the challenges for experimenters increase accordingly.

We place practitioners’ methodologies, experimental designs, strategies of inquiry, and practices of implementation in the center of our analyses. We thereby draw new trajectories and connections in the history of experimental inquiry. We identify lines of experimentation that sometimes turned into models of rigorous experimental design while other times being criticized. Bacon’s steeping experiments with plant seeds, as analyzed by Pastorino, exemplify a specific kind of comparative experimentation. It would be applied again and again throughout the eighteenth century, not just in plant science but also in other scientific fields. Pythagoras’ hammer experiments too were repeated, at least repeatedly reported, by several scholars prior to Galileo and Mersenne. In this case, the design was not a model but a point of critique for later scholars.

Our studies on control practices and on their discussion and justification have revealed other lineages and cross-fertilizations—among physics and psychology, physiology, botany and ethology, chemistry, medicine, agriculture, and philosophy. Control practices and strategies are contextual, in that the context determines what is controlled and how to achieve control. But control strategies and at times even control practices are not discipline-specific. The same strategies travel across disciplines, from physics to medicine and physiology to chemistry and back again. Several chapters suggest that the same methodological ideas and control strategies are advocated across national boundaries (see especially Schürch and Coko). Control strategies such as comparative designs and multiple repetitions are relatively stable across historical periods. But they may be justified in different ways at different times and may cease to be justified at all.

With our work, we hope to stimulate broader discussions about the longer-term history of rigorous experimentation: what are the strategies involved in it? And how do debates concerning well-designed experiments unfold in different fields and periods? By our effort we seek to clarify the roles of experimental strategies and methodologies as driving forces for scientific change, and as tools for determining what it means to do—or not to do—good science.

This volume (and its companion, a collection of essays on analysis and synthesis) originated in a Sawyer Seminar at Indiana University Bloomington titled “Rigor: Control, Analysis and Synthesis in Historical and Systematic Perspectives,” which was funded by the Andrew W. Mellon Foundation. Mellon Sawyer Seminars are temporary research centers, gathering together faculty, postdoctoral fellows, and graduate students for in-depth study of a scholarly subject in reading groups, seminars, and workshops. As part of our activities, we organized two international conferences. They brought together scholars in history, philosophy, and social studies of science who examine historical and contemporary dimensions of rigor in experimental practice. The contributors to this volume participated in the second of the Sawyer conferences (March 2022) and reconvened a few months later for an authors’ workshop, at which the draft chapters for this volume were intensely discussed.

Several institutions and individuals helped to make our work possible. We gratefully acknowledge the Mellon Foundation’s generous financial support, and especially the Foundation’s flexibility as we dealt with the challenges of pursuing collaborative scholarship during a pandemic. We are grateful to Director of Foundation Relations Cory Rutz at Indiana University’s Office of the Vice President for Research, for his prompt and efficient assistance in administering the grant. The authors’ workshop took place at the IU Europe Gateway (Berlin) and was funded by a combined grant from the IU College of Arts and Sciences and the College Arts and Humanities Institute. We very much appreciate this support. We are indebted to Jed Buchwald for including our work in the Archimedes series, and to Chris Wilby for his efforts in moving the publication along. A big thank you to our department manager Dana Berg (Department of History and Philosophy of Science and Medicine at IU), office assistant Maggie Herms (IU HPSC), and Andrea Adam Moore (IU Europe Gateway), all of whom helped to organize our conferences and workshops. Finally, we warmly thank the many participants at the two conferences and at the various other Sawyer events for their valuable input, comments, questions, and critique.

This is slowly changing, see Guettinger ( 2019 ); Sullivan ( 2022 ); Guettinger ( 2019 ); Desjardins et al. ( 2023 ).

Only the randomized controlled trial has been studied historically and systematically. See Marks ( 1997 , Chap. 5); Worrall ( 2007 ); Cartwright ( 2007 ); Keating and Cambrosio ( 2012 ). For the control group and (double) blind tests, see Kaptchuk ( 1998 ); Strong and Frederick ( 1999 , including further references); Dehue ( 2005 ); Holman ( 2020 ).

For a variety of views, see, for instance, McCartney ( 1942 ); Beniger ( 1986 ); Levin ( 2000a , 13–14); Amici ( 2001 ).

Two classic studies of how experimenters sought to “control” their audiences are Shapin and Schaffer ( 1985 ) and Geison ( 1995 , especially Chap. 5).

These ideas are also articulated in the philosophy of science, of course. In this volume, however, we are concerned mostly with practicing experimenters’ working philosophies.

Some historians have strong reservations about long-term histories “lining up unconnected look-alikes through the ages” (Dehue 2005 , 2), or “ahistorical narratives” comparing, for instance, early modern and Victorian experiments “merely because of superficial similarity ‘in the use of controls’” (Strick 2000 , 5, commenting on spontaneous generation experiments). Our volume shows that it is possible to write long-term histories without comparing apples to oranges.

These distinctions are inspired by one of the few systematic studies of controlled experimentation, Edwin Boring’s “The Nature and History of Experimental Control” (Boring 1954 ).

This insight underlies Ludwik Fleck’s and Thomas Kuhn’s accounts of scientific change.

Comparison, Boring noted, “appears in all experimentation because a discoverable fact is a difference or a relation, and a discovered datum has significance only as it is related to a frame of reference, to a relatum” (Boring 1954 , 589).

For the epistemic ideal underlying this design, the “perfectly controlled experiment,” see Guala ( 2005 , 65–69).

I keep this characterization vague because I do not want to commit to a specific philosophical understanding of causality here.

Technical terms such as “positive” and “negative” control are even more recent (and outside the timeframe of our volume). They are also poorly understood.

For a brief overview of historical definitions of control, see Levin ( 2000a , 21–31).

See Holbrook ( 2009 ).

See, e.g., Kuch et al. ( 2020 ).

The quotation is drawn from Kursell’s chapter in this volume.

Much of the text rebuts the sorites argument, according to which it is impossible to clarify the notion of seeing something “very many times” (see Galen 1944 , 124–25). For a reconstruction of the argument, see (Kupreeva 2022 ).

For the Aristotelian notion of the memory of many instances, see Bayer ( 1997 ). For its application in the scholastic-mathematical tradition, see Dear ( 1991 ).

On repetition and “many, many” trials, see Schickore ( 2017 , chapters 1–3).

A popular passage by Karl Popper expresses this idea: “Every experimental physicist knows those surprising and inexplicable apparent ‘effects’ which in his laboratory can perhaps even be reproduced for some time, but which finally disappear without trace. Of course, no physicist would say in such a case that he had made a scientific discovery (though he might try to rearrange his experiments so as to make the effect reproducible). Indeed the scientifically significant physical effect may be defined as that which can be regularly reproduced by anyone who carries out the appropriate experiment in the way prescribed. No serious physicist would offer for publication, as a scientific discovery, any such ‘occult effect,’ as I propose to call it—one for whose reproduction he could give no instructions. The ‘discovery’ would be only too soon rejected as chimerical, simply because attempts to test it would lead to negative results. (It follows that any controversy over the question whether events which are in principle unrepeatable and unique ever do occur cannot be decided by science: it would be a metaphysical controversy)” (Popper 2002 , 23–24).

This example is also quoted on the website of the Institute for Creation Research as a model for sound experimental design (Treece 1990 ).

Deipnosophists or Banquet of the Learned, 3.84 d-f:2. The reference is from McCartney ( 1942 , 5–6).

For details on Redi’s experiments, see Parke ( 2014 ). Historians of biology as well as science educators regularly cite Redi’s experiments on spontaneous generation as “the first control experiments.”

In his well-known book on Pasteur, Gerald Geison drew on Pasteur’s experiments with infusions to show that the negotiations of what does and does not count as a properly controlled experiment in the spontaneous generation debates turned into battles motivated by political and religious concerns. Geison argues that Pasteur effectively “controlled” his audiences (Geison 1995 ).

Bertoloni Meli ( 2009 ) describes many other comparative experiments from the early modern period. See also Schickore ( 2021 ).

Our volume focuses on practitioners’ methodological accounts. However, even in philosophy of science, Mill had predecessors in this regard: Dugald Stewart and John Herschel, for instance, cover territory very similar to Mill’s four methods of experimental inquiry.

For another example of appeals to the community in the struggle to identify the causes of blue milk, see Schickore ( 2023 , 37).

See Schürch’s discussion of Ingen-Housz in this volume, for example.

For another example of how control practices themselves become the object of study, see Landecker ( 2016 ).

Researchers today have identified other areas of concern for over-emphasizing rigor and control. One example is over-standardized mice (Engber 2013 ), and these studies highlight the importance of balancing control with other demands on research design. In public health studies, researchers must overcome barriers for recruitment, attrition, and sample size, which may necessitate lowering the bar for rigor to gather any valuable information at all (Crosby et al. 2010 ). Thus, the implication of an over-emphasis on rigor may be epistemic, socio-political, or both.

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Experimental controls

A control is a comparison.

In an experiment , you test a hypothesis by adjusting something - an independent variable . A control is something you put through the same procedure, without adjusting that independent variable. You can then compare the results of the control to the other results of the experiment.

We will use an analogy to explain the concept of experimental controls

Imagine you are standing on a hill, which is surrounded by several other similar hills and you start wondering if it would be possible to climb one of those nearby hills with your shoelaces tied together – an obvious thought when one looks at a beautiful panorama!

Luckily, you don’t have to wonder for long because you are accompanied by three boy scouts.

You choose the following experimental setup.

Each boy scout will walk up a hill and light a fire as soon as they reach the top. This way you will be able to see and record the result from a distance.

You are a skilled scientist and know that you have to introduce controls to support your results. Using experimental controls is the way of knowing if your results are due to the variable you are testing, or caused by the experimental procedure itself.

Figure 1: Scout boy analogy.

Following the example, you have no idea what awaits your little boy scouts down in the forest. There could be a river or a hungry bear in that valley. You would never know if it were the tied shoelaces that stopped them from making the fire or something else. To avoid such uncertainties, you decide to send off one of the boy scouts as a positive control. You won’t tie his laces together. This should guarantee that he would be able to make it up to one of the hills and light a fire. If there is no fire on that hill, you know there is something wrong with your experimental setup. This is an excellent positive control.

For creating your negative control, you become very creative and find an interdisciplinary approach directly from the Mafioso Repertoire. You fill a bucket with concrete and use it to immobilize one boy scout. You know with such a weight on his feet he might be able to hop down the valley, but he will never make it up the other hill.

Again, you have no idea what will happen down in that valley. You ensured that the shoes of all your boy scouts are well tied and can’t be easily taken off. But in the case they do find a way to take off the shoes and walk up barefoot, you know the boy with his cement shoes would do the same and there would be a fire on his hill as well.

Perfect, this is all you need to run the experiment. With such an excellent setup, you can lean back and wait for the fire signals.

Whatever experiment you are performing, you should always come up with good controls that are truly comparable to the experimental sample. Sending the positive control off with a helicopter would be a useless positive control, just like tying the third boy scout to a tree would be a bad negative control.

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Academic Recruitment Guide

This page is intended for use by students and researchers in the University of Cambridge Schools of Technology and Physical Sciences whose research involves recruiting people from outside your own research team to take part in experiments. It is part of a larger set of research guidance pages on work with human participants.

Issues to note for ethical review

This page gives general guidance relating to conduct of experiments. The following issues are particularly relevant with regard to ethical review:

Recruitment
Treatment of Participants
Informed Consent

Data Retention

Incentives and Compensation

Definitions

A controlled experiment is an experimental setup designed to test hypotheses .

A controlled experiment has one or more conditions (independent variables) and measures (dependent variables).

A randomised controlled trial is an experiment in which participants are assigned at random to different conditions, in order to test in an objective way which of several alternatives is superior.

A pilot study is a trial run of an experimental procedure, not expected to produce valid research data.

Controlled experiments may or may not require human participants. This page is only about controlled experiments involving human participants.

Introduction - Controlled experiments

Controlled experiments are difficult to design and analyse. Students in experimental psychology take practical classes in experiment design before they attempt to conduct their own original research. However, all experiments with human participants conducted by students in Technology and Physical Sciences have the character of original research, from a psychology perspective. It is therefore a common experience for technology researchers to find that their first experiment produces meaningless or null results, often after a great deal of effort. This is wasteful of time and resources, both for the researchers and participants, so should be avoided. For this reason controlled experiments should only be carried out by researchers who are trained in experimental design and analysis, or under direct supervision of researchers with suitable training. If you have little experience you should consult senior researchers.

Some important considerations include design for:

Reliability (Would you get the same measurement again?)
Validity (Are you measuring what you claim to be measuring?)
Internal validity is the relationship between your measurement and what you think it tells you about the experimental task.
External validity is the relationship between what you measure in the lab, and the phenomenon in the outside world.

A well-known reference book is:

Krik, R.E. Experimental Design: Procedures for Behavioral Sciences.

Practicalities - Controlled experiments

Preparation, experimental design.

It is easy to make serious errors when you first attempt to design a controlled experiment. There are many textbooks and online guides - make use of them. Ask an expert to review your experimental design , and try it out in advance with several pilot studies .

There are a number of critical factors that could cause the experimental results to be invalid, and it is important to anticipate these and avoid them. One way to do so is to plan, in advance, how you propose to write up the results of the experiment. Think about the conclusions that you would draw if the result of the experiment is consistent with your hypothesis. How would you present your results in a way that convinces the reader that conclusion is justified? What would the results of data analysis have to be to support this kind of presentation? What experimental method will produce data that can be analysed in this way? What is the best way to express an hypothesis compatible with that method? If you can explain your reasoning in this way, before you start the experiment, you will have a much better chance of avoiding the invalid and/or inconclusive results that are so often obtained by inexperienced experiment designers.

Pilot studies

It is very hard to get an experimental procedure right the first time. Every experiment should therefore include at least one pilot session, with a participant whose results you expect to discard from the final data analysis. For this reason, it is common to use a pilot subject whose results you would not expect to be valuable - for example, because they are aware of the experimental hypothesis, have specialist expertise, or similar. Family members and (fellow) students can be useful.

Where an experimental paradigm is unconventional, or there is substantial uncertainty about either the measures or the hypotheses, you should consider a pilot study involving several participants, in which each of the experimental conditions is used, and a preliminary data analysis can be conducted.

Recruitment

In order for research to have good external validity, the recruited participants should be representative of the population about which you want to make research conclusions. However, in practice, undergraduate and graduate students are often recruited because this is easier. If you plan to do this, it is a good idea to think in advance how you will justify it to reviewers or assessors of your work.

Where children are involved in research, recruitment is likely to be via schools or parents. Some experiments with children, or with vulnerable adults, may also require that members of the research team undergo a Disclosure and Barring Service (DBS) check .

Where participants have been recruited on the basis of a medical condition, it is likely that your research will require approval via the NHS Research Ethics Service .

It is increasingly common to recruit experimental participants via platforms such as Amazon Mechanical Turk or Figure Eight (formerly CrowdFlower). There are many distinct ethical implications of experiments conducted using these tools that are rather different to those arising in the conduct of experiments in a laboratory. For further guidance, see the page on Crowd sourcing experiments

Conducting the experiment

Treatment of participants.

In most experiments, participants are asked to carry out an experimental task while being observed, or while their responses are being measured. It is of paramount importance that participants are treated with dignity and respect. Remember that you are in a position of power from the participants' perspective. You need to inform yourself about participants' rights and then disclose these rights to the participants. Among those rights:

The right to stop participating in the experiment, possibly without giving a reason.
The right to obtain further information about the purpose and the outcomes of the experiment.
The right to have their data anonymised.

This list is not exhaustive.

It is often the case that people being asked to use new technologies while under observation find the experience stressful. It is very important to reassure participants that your objective is to identify possible faults in the technology, and not to test the participants' own ability or intelligence. If they have trouble completing an experimental task, you should reassure them further, emphasising that they have had this experience because the technology is inadequate, and that it is not a reflection on their own ability. Experimenters should never offer any comment with regard to participants' intelligence, aptitude, or other factors that might give people the impression that a scientific judgment of their ability has been performed. This is especially the case if standard psychometric tests are being employed as one of the experimental measures. An experimental situation in technology or physical sciences is not a proper psychometric assessment, and psychometric test results should not be directly communicated to participants.

Informed consent

It is very important for participants to understand that their participation in the experiment is completely voluntary. In order to ensure that they understand this, experimenters should prepare a 'consent form', stating the nature of the experiment and the rights of the participant. Before the start of the experiment, participants should be asked to read this form, and sign it to indicate that they have read and understood their rights. An example consent form can be found on the University Research Ethics pages .

You may wish to assure participants that no personal data is collected, or if it is collected, that it will not be published, and will be destroyed. These things can be mentioned in a consent form.

If a participant appears to be experiencing any stress (for example due to task difficulty, or perhaps through factors unrelated to the experiment), it is important to remind them that they are free to withdraw at any time.

If a participant is experiencing physical pain (e.g. because of extensive use of the mouse for the task) then abort the experiment immediately and consult a senior colleague or the appropriate university ethics committee for advice on whether to proceed with the experimental procedure.

In the case of children (in the UK, under the age of 18), consent must be given by a parent. The experimenter may also be subject to a Disclosure and Barring Service (DBS) check.

Participant briefing

For the purposes of experimental control, every participant should be given the same instructions before they commence the experimental task. Briefing instructions are normally written out in full, in order to ensure that this is done. The instructions can either be read from a script by the experimenter, or given to the participant to read, after which they are asked if they have understood everything, and are ready to start.

If an experimenter script is used, it is a good idea for this to include all instructions and actions that the experimenter must carry out throughout the experimental session. This script should be tested during the experimental pilot, and helps gain maximum value from the pilot as a 'debugging' session for the main experimental procedure.

At the end of an experimental session, participants should normally be debriefed. Debriefing involves a short interview, often semi-structured, with some prepared questions that you ask every participant, and follow-up questions in the event that interesting points are raised.

This provides a valuable data collection opportunity, especially as participants' subjective experience of the experiment could be of value in interpreting either their individual performance, or behaviour observed more broadly across the sample group. It may be useful to discuss your experimental hypothesis with participants, because they might well be able to warn you of potential problems with task validity, from their perception of the task.

Whether or not you expect to gain useful information for research purposes, debriefing also provides an opportunity for the participant to reflect on the experience they have had. It is a good idea to complete the debriefing interview by asking whether there is anything else the participant would like to tell you.

Incentives and compensation

It is recommended to compensate participants for their time, although compensation need not be financial . People may be very willing to participate in experiments from which they gain interesting feedback, or experiments that are intrinsically enjoyable (for example games). A token gift (chocolates, a book or report, software, or a memento such as copies of a scan) may be sufficient reward. Nevertheless, many departments in Cambridge routinely recruit experimental participants, and payment may be expected after a formal experiment. If the participant has incurred direct costs such as travel these should be reimbursed.

If a participant chooses to withdraw, or not to complete the experiment, they should still be compensated. Experiments in which incentive payments are varied according to task performance are considered to be unethical. A standard procedure where incentive is a central hypothesis (for example experiments in economic judgment) is to offer participants variable payment at the outset, but then to pay all participants the same (usually maximum) amount at the close of the session.

The university has issued rules on procedure to be used, and how much compensation should be given to participants. Finance division policy on payments to research volunteers is described here.

If the data collected does not include any personal data, then the data may be retained. If they do contain personal data, then they fall within the terms of the Data Protection Act. Personal data should be kept secure. Data that would allow a participant to be identified should be kept in a separate place throughout the research project, with an anonymised code used during analysis work and at publication time. It is good practice to destroy any personal data after a stated period of time. In most cases, experimental data is used only by the person conducting the experiment. If this is not the case, see the page on academic research involving personal data .

Significant ethical issues

This page is intended to address relatively routine research in the schools of Technology and Physical Sciences. If your experiment involves any of the following activities, then more serious questions must be addressed, and you will need to consult the relevant university ethics committee:

Experiments involving animals are subject to the animals scientific procedures act
Medical and other invasive experiments on human participants must be reviewed by the NHS research ethics service .
Psychological manipulation of human participants (deception, emotional manipulation, etc.).

This list is not exhaustive. When in doubt consult senior colleagues and relevant university ethics committees.

Some popular books are:

Kirk, R.E. Experimental Design: Procedures for Behavioral Sciences.
Robson, C. Experiment, Design and Statistics in Psychology

Future information: to include references to appropriate Cambridge courses on research and experimental design in Social Psychology, Experimental Psychology etc.

The initial version of this page was drafted by Per Ola Kristensson.

All comments and feedback are welcome. Please send any feedback to [email protected]

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Control Group vs Experimental Group

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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In a controlled experiment , scientists compare a control group, and an experimental group is identical in all respects except for one difference – experimental manipulation.

Differences

Unlike the experimental group, the control group is not exposed to the independent variable under investigation. So, it provides a baseline against which any changes in the experimental group can be compared.

Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

Almost all experimental studies are designed to include a control group and one or more experimental groups. In most cases, participants are randomly assigned to either a control or experimental group.

Because participants are randomly assigned to either group, we can assume that the groups are identical except for manipulating the independent variable in the experimental group.

It is important that every aspect of the experimental environment is the same and that the experimenters carry out the exact same procedures with both groups so researchers can confidently conclude that any differences between groups are actually due to the difference in treatments.

Control Group

A control group consists of participants who do not receive any experimental treatment. The control participants serve as a comparison group.

The control group is matched as closely as possible to the experimental group, including age, gender, social class, ethnicity, etc.

The difference between the control and experimental groups is that the control group is not exposed to the independent variable , which is thought to be the cause of the behavior being investigated.

Researchers will compare the individuals in the control group to those in the experimental group to isolate the independent variable and examine its impact.

The control group is important because it serves as a baseline, enabling researchers to see what impact changes to the independent variable produce and strengthening researchers’ ability to draw conclusions from a study.

Without the presence of a control group, a researcher cannot determine whether a particular treatment truly has an effect on an experimental group.

Control groups are critical to the scientific method as they help ensure the internal validity of a study.

Assume you want to test a new medication for ADHD . One group would receive the new medication, and the other group would receive a pill that looked exactly the same as the one that the others received, but it would be a placebo. The group that takes the placebo would be the control group.

Types of Control Groups

Positive control group.

A positive control group is an experimental control that will produce a known response or the desired effect.
A positive control is used to ensure a test’s success and confirm an experiment’s validity.
For example, when testing for a new medication, an already commercially available medication could serve as the positive control.

Negative Control Group

A negative control group is an experimental control that does not result in the desired outcome of the experiment.
A negative control is used to ensure that there is no response to the treatment and help identify the influence of external factors on the test.
An example of a negative control would be using a placebo when testing for a new medication.

Experimental Group

An experimental group consists of participants exposed to a particular manipulation of the independent variable. These are the participants who receive the treatment of interest.

Researchers will compare the responses of the experimental group to those of a control group to see if the independent variable impacted the participants.

An experiment must have at least one control group and one experimental group; however, a single experiment can include multiple experimental groups, which are all compared against the control group.

Having multiple experimental groups enables researchers to vary different levels of an experimental variable and compare the effects of these changes to the control group and among each other.

Assume you want to study to determine if listening to different types of music can help with focus while studying.

You randomly assign participants to one of three groups: one group that listens to music with lyrics, one group that listens to music without lyrics, and another group that listens to no music.

The group of participants listening to no music while studying is the control group, and the groups listening to music, whether with or without lyrics, are the two experimental groups.

Frequently Asked Questions

1. what is the difference between the control group and the experimental group in an experimental study.

Put simply; an experimental group is a group that receives the variable, or treatment, that the researchers are testing, whereas the control group does not. These two groups should be identical in all other aspects.

2. What is the purpose of a control group in an experiment

A control group is essential in experimental research because it:

Provides a baseline against which the effects of the manipulated variable (the independent variable) can be measured.

Helps to ensure that any changes observed in the experimental group are indeed due to the manipulation of the independent variable and not due to other extraneous or confounding factors.

Helps to account for the placebo effect, where participants’ beliefs about the treatment can influence their behavior or responses.

In essence, it increases the internal validity of the results and the confidence we can have in the conclusions.

3. Do experimental studies always need a control group?

Not all experiments require a control group, but a true “controlled experiment” does require at least one control group. For example, experiments that use a within-subjects design do not have a control group.

In within-subjects designs , all participants experience every condition and are tested before and after being exposed to treatment.

These experimental designs tend to have weaker internal validity as it is more difficult for a researcher to be confident that the outcome was caused by the experimental treatment and not by a confounding variable.

4. Can a study include more than one control group?

Yes, studies can include multiple control groups. For example, if several distinct groups of subjects do not receive the treatment, these would be the control groups.

5. How is the control group treated differently from the experimental groups?

The control group and the experimental group(s) are treated identically except for one key difference: exposure to the independent variable, which is the factor being tested. The experimental group is subjected to the independent variable, whereas the control group is not.

This distinction allows researchers to measure the effect of the independent variable on the experimental group by comparing it to the control group, which serves as a baseline or standard.

Bailey, R. A. (2008). Design of Comparative Experiments. Cambridge University Press. ISBN 978-0-521-68357-9.

Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.

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Control Set-up: Acing Experiment-Based Questions in PSLE Science

This is the last of 5 articles on experiment-based questions in PSLE Science . Experimental-based questions are regularly tested in Science exam papers and it is important that students have mastery over the concepts tested.

The focus for today’s article is on control set-ups, another question type dreaded by students. They either do not understand the purpose of a control set-up or do not know how to draw a control set-up. Here, I will give you two specific examples on how to tackle questions involving control set-ups.

Purpose of a Control Set-up

It is used for comparison in order to verify the conclusion of an experiment.
To ensure that the measured variable (dependent variable) is affected only by the changed variable (independent variable) and not any other (control) variables in the experiment.

Scientist Strange carried out an experiment as shown below. He left both set-ups in a warm and dark cupboard for two days. He observed that the seeds in Set-up X germinated and the limewater turned cloudy while the limewater in Set-up Y remained clear.

What is the aim of the experiment and purpose of having Set-up Y?

The only changed variable (independent variable) → presence of seeds
Measured variable (dependent variable) → if carbon dioxide was given out by plant
Control variable → all other variables in the experiment like size of beakers, amount of limewater, presence of rubber stoppers in both beakers, same location where experiment was carried out, etc.
Since the aim of an experiment is related to the independent and dependent variable, the aim of the experiment is to find out if germinating seeds give out carbon dioxide .
Purpose of Set-up Y → A control set-up to confirm that carbon dioxide was given out by the germinating seeds .

How to Draw the Control Set-up for an Experiment?

A good way to know how to draw the control set-up is to change the independent variable in the control set-up so as to prove that any change in result is only due to the independent variable and not because of other control variables .

This concludes the last of 5 articles on Experiment-Based Questions that are tested in school examinations and PSLE Science paper. With a subscription to OwlSmart , students get access over 200 experiment-based questions for PSLE Science with concise explanations. Students who practised more on these questions have shown to have a higher level of confidence in tackling them in the PSLE.

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The Difference Between Control Group and Experimental Group

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In an experiment , data from an experimental group is compared with data from a control group. These two groups should be identical in every respect except one: the difference between a control group and an experimental group is that the independent variable is changed for the experimental group, but is held constant in the control group.

Key Takeaways: Control vs. Experimental Group

The control group and experimental group are compared against each other in an experiment. The only difference between the two groups is that the independent variable is changed in the experimental group. The independent variable is "controlled", or held constant, in the control group.
A single experiment may include multiple experimental groups, which may all be compared against the control group.
The purpose of having a control is to rule out other factors which may influence the results of an experiment. Not all experiments include a control group, but those that do are called "controlled experiments."
A placebo may also be used in an experiment. A placebo isn't a substitute for a control group because subjects exposed to a placebo may experience effects from the belief they are being tested; this itself is known as the placebo effect.

What Are Is an Experimental Group in Experiment Design?

An experimental group is a test sample or the group that receives an experimental procedure. This group is exposed to changes in the independent variable being tested. The values of the independent variable and the impact on the dependent variable are recorded. An experiment may include multiple experimental groups at one time.

A control group is a group separated from the rest of the experiment such that the independent variable being tested cannot influence the results. This isolates the independent variable's effects on the experiment and can help rule out alternative explanations of the experimental results.

While all experiments have an experimental group, not all experiments require a control group. Controls are extremely useful where the experimental conditions are complex and difficult to isolate. Experiments that use control groups are called controlled experiments .

A Simple Example of a Controlled Experiment

A simple example of a controlled experiment may be used to determine whether or not plants need to be watered to live. The control group would be plants that are not watered. The experimental group would consist of plants that receive water. A clever scientist would wonder whether too much watering might kill the plants and would set up several experimental groups, each receiving a different amount of water.

Sometimes setting up a controlled experiment can be confusing. For example, a scientist may wonder whether or not a species of bacteria needs oxygen in order to live. To test this, cultures of bacteria may be left in the air, while other cultures are placed in a sealed container of nitrogen (the most common component of air) or deoxygenated air (which likely contained extra carbon dioxide). Which container is the control? Which is the experimental group?

Control Groups and Placebos

The most common type of control group is one held at ordinary conditions so it doesn't experience a changing variable. For example, If you want to explore the effect of salt on plant growth, the control group would be a set of plants not exposed to salt, while the experimental group would receive the salt treatment. If you want to test whether the duration of light exposure affects fish reproduction, the control group would be exposed to a "normal" number of hours of light, while the duration would change for the experimental group.

Experiments involving human subjects can be much more complex. If you're testing whether a drug is effective or not, for example, members of a control group may expect they will not be unaffected. To prevent skewing the results, a placebo may be used. A placebo is a substance that doesn't contain an active therapeutic agent. If a control group takes a placebo, participants don't know whether they are being treated or not, so they have the same expectations as members of the experimental group.

However, there is also the placebo effect to consider. Here, the recipient of the placebo experiences an effect or improvement because she believes there should be an effect. Another concern with a placebo is that it's not always easy to formulate one that truly free of active ingredients. For example, if a sugar pill is given as a placebo, there's a chance the sugar will affect the outcome of the experiment.

Positive and Negative Controls

Positive and negative controls are two other types of control groups:

Positive control groups are control groups in which the conditions guarantee a positive result. Positive control groups are effective to show the experiment is functioning as planned.
Negative control groups are control groups in which conditions produce a negative outcome. Negative control groups help identify outside influences which may be present that were not unaccounted for, such as contaminants.
Bailey, R. A. (2008). Design of Comparative Experiments . Cambridge University Press. ISBN 978-0-521-68357-9.
Chaplin, S. (2006). "The placebo response: an important part of treatment". Prescriber : 16–22. doi: 10.1002/psb.344
Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
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What is a control setup ?

A control setup in science uses the same conditions and the same equipment as the experimental setup; however, there are no variables tested in the control setup, as there are in the experimental setup. a control setup can include the use of a control group, which takes place when the experiment includes people. the people in the control group act as a control set-up. they do not receive the factor or active medication that the people do in the experimental group, which acts as the experimental setup. a controlled experiment can use a control group or a controlled setup, but is designed so that only one variable is manipulated at a time. this is necessary for the experiment to produce accurate results because if there are multiple variables then the scientists cannot know which variable produced which result. the scientific method is used in the experimental process and in a controlled setup. the scientific method has several steps, which are: ask a question, do background research, construct a hypothesis, test the hypothesis by doing an experiment, analyze the data and draw a conclusion and communicate the results. the scientific method is the method by which all experiments are conducted and allows scientists to ask and answer scientific questions through observations and experiments..

Fluorination of Methane can be controlled in a Laboratory setup - True or False?

In the above setup, what is X?

What is the inference of the experimental setup shown below?

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What Is a Control Setup in a Scientific Experiment?
An experimental setup is conducted in the same manner as the control, but it includes one aspect to be measured. A control setup uses all the same equipment under the same conditions, but no variables are tested, and it provides a baseline with which to compare the results of the experiment setup. A scientific experiment has several parts ...
What Is a Control Setup in Science?
The people in the control group act as a control set-up. They do not receive the factor or active medication that the people do in the experimental group, which acts as the experimental setup. A controlled experiment can use a control group or a controlled setup, but is designed so that only one variable is manipulated at a time.
Tackle Control Set-up Questions Like A Pro
A control set-up is often needed to ensure that the experiment is valid & accurate. It serves as a baseline for the comparison of the results of the main experiment. The control set-up will also rule out other environmental variables from affecting the results and eventually help to establish the conclusion of the experiment.
Controlled Set-up Definition and Example
Join this channel to get access to perks:https://www.youtube.com/channel/UCq1EXFAsOSEuaDU9nICu8QQ/joinAWESOME SCIENCE EXPERIMENTS: https://amzn.to/3Yb2BsA, h...
What Is a Controlled Experiment?
Control groups allow you to test a comparable treatment, no treatment, or a fake treatment (e.g., a placebo to control for a placebo effect), and compare the outcome with your experimental treatment. You can assess whether it's your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in ...
Why control an experiment?
the experimental setup and measuring tools: The negative control of an enzyme assay, for instance, tests for any unrelated back-ground signals from the assay or measure-ment. In short, controls are essential for the unbiased, objective observation and measurement of the dependent variable in response to the experimental setup.
Exam skill video
In this video, you will learn tips on distinguishing the experimental set-up and control set-up, and also understand the functions of various set-ups in a co...
Experimental Setup
An experimental setup refers to the specific arrangement and conditions in which an experiment is conducted to investigate a hypothesis or research question. It involves manipulating independent variables, measuring dependent variables, and controlling extraneous factors. ... Control Group: A control group is a group in an experiment that does ...
HOW TO TACKLE CONTROL SET-UP QUESTIONS
Control Set-up : Experimental-based Questions Experimental-based questions are regularly tested in Primary School Science examinations. In this article I will share with you three question samples on how to tackle questions involving control set-ups by using " 3C " method ( C ontrol -C ompare -C onfirm ) .
3D-cell phantom-experimental setup to assess thermal effects and cell
A: Experimental setup for an EP procedure including the electrode mounting interface positioned on a rail and the Bio-Phantom; B: Cross-section of the electrode mounting interface including the ...
Understanding experimental setup questions
Understanding experimental setup questions. Experimental setup questions are a type of open-ended questions that assess your child's understanding of scientific experiments. They are not hands-on experiments where your child will be expected to mix chemicals together; rather, these questions require them to analyse and explain the setup ...
Introduction: Practices, Strategies, and Methodologies of Experimental
The history of experimental control, then, encompasses four distinct yet related strands. The first is the historical development of control practices to stabilize and standardize experimental conditions. The second is the emergence and career of the comparative design in experimentation, understood as a way of generating and securing knowledge ...
Experimental controls
A control is a comparison. In an experiment, you test a hypothesis by adjusting something - an independent variable. A control is something you put through the same procedure, without adjusting that independent variable. ... You choose the following experimental setup. Each boy scout will walk up a hill and light a fire as soon as they reach ...
CONTROL GROUP / EXPERIMENTAL GROUP / CONTROL SET-UP ...
VL #2 - RESEARCHThis video lesson discusses CONTROL AND EXPERIMENTAL GROUP, in a very simple manner and comprehensive examples. It is an interactive video b...
Controlled Experiments
Definitions. A controlled experiment is an experimental setup designed to test hypotheses. A controlled experiment has one or more conditions (independent variables) and measures (dependent variables). A randomised controlled trial is an experiment in which participants are assigned at random to different conditions, in order to test in an ...
Control Group Vs Experimental Group In Science
In a controlled experiment, scientists compare a control group, and an experimental group is identical in all respects except for one difference - experimental manipulation.. Differences. Unlike the experimental group, the control group is not exposed to the independent variable under investigation. So, it provides a baseline against which any changes in the experimental group can be compared.
Guide to Experimental Design
Table of contents. Step 1: Define your variables. Step 2: Write your hypothesis. Step 3: Design your experimental treatments. Step 4: Assign your subjects to treatment groups. Step 5: Measure your dependent variable. Other interesting articles. Frequently asked questions about experiments.
Control Set-up: Acing Experiment-Based Questions in PSLE Science
Experimental-based questions are regularly tested in Science exam papers and it is important that students have mastery over the concepts tested. The focus for today's article is on control set-ups, another question type dreaded by students. They either do not understand the purpose of a control set-up or do not know how to draw a control set-up.
The Difference Between Control Group and Experimental Group
The control group would be plants that are not watered. The experimental group would consist of plants that receive water. A clever scientist would wonder whether too much watering might kill the plants and would set up several experimental groups, each receiving a different amount of water.
What is a control setup
A control setup can include the use of a control group, which takes place when the experiment includes people. The people in the control group act as a control set-up. They do not receive the factor or active medication that the people do in the experimental group, which acts as the experimental setup. A controlled experiment can use a control ...
Experimental Set-up Definition and Example
Join this channel to get access to perks:https://www.youtube.com/channel/UCq1EXFAsOSEuaDU9nICu8QQ/joinAWESOME SCIENCE EXPERIMENTS: https://amzn.to/3Yb2BsA, h...
Numerical Modelling and Experimental Validation of Selective Laser
The comprehensive experimental setup, with its iterative improvements and precise control over laser and environmental parameters, was crucial for validating the thermal model. The high-quality data obtained from these experiments provided a robust foundation for comparison with the simulated results, ensuring the accuracy and reliability of ...

What Is a Control Setup in Science?

Tackle Control Set-up Questions Like A Pro

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3D-cell phantom-experimental setup to assess thermal effects and cell viability of lung tumor cells after electroporation

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Experimental controls

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