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Related Concept Videos

Controls in Experiments01:13

Controls in Experiments

When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a...
What is an Experiment?01:12

What is an Experiment?

An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. 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...

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A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies
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Harnessing complexity may strangle it: what is a well-controlled experiment?

Jutta Schickore1,2, Sara Magalhães2,3, Mariana Gómez-Schiavon2,4,5

  • 1Department of History and Philosophy of Science and Medicine, Indiana University, Bloomington, IN, USA.

Proceedings. Biological Sciences
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

Scientists should aim to "control well" in biological experiments, not just strictly. Balancing fixed variables with some freedom leads to better learning from complex biological systems.

Keywords:
causalitycontrolemergent propertiesexperimental design

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Area of Science:

  • Biological experimentation
  • Philosophy of science

Background:

  • The ideal of a perfectly controlled experiment is a core principle in biological research.
  • Scientists generally strive to approximate this ideal, even though it's practically unattainable.

Purpose of the Study:

  • To critically examine the epistemological basis for the ideal of strict experimental control.
  • To propose an alternative framework for understanding and implementing control in biological experiments.

Main Methods:

  • Distinguishing various ways real-world experiments deviate from perfect control.
  • Analyzing the impact of strict control on experimental outcomes in biological systems.

Main Results:

  • Strict control does not always lead to superior experimental results.
  • Overly strict control can be counterproductive for understanding complex biological systems.
  • Biological systems' inherent complexity necessitates a more nuanced approach to control.

Conclusions:

  • The concept of "controlling well" is proposed as a more pragmatic and effective approach.
  • "Controlling well" involves balancing the fixation of variables with the allowance of degrees of freedom.
  • The optimal level of control is context- and purpose-dependent for each experimental project.