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Scientific control: the story on HearLore | HearLore
— Ch. 1 · Foundations Of Control —
Scientific control.
~6 min read · Ch. 1 of 6
Take identical growing plants of the species Argyroxiphium sandwicense and give fertilizer to half of them. If there are differences between the fertilized treatment and the unfertilized treatment, these differences may be due to the fertilizer as long as there weren't other confounding factors that affected the result. A scientific control is an element of an experiment or observation designed to minimize the influence of variables other than the independent variable under investigation. This process reduces the risk of confounding by ensuring that only the factor being tested changes while all others remain constant. The use of controls increases the reliability and validity of results by providing a baseline for comparison between experimental measurements and control measurements. In many designs, the control group does not receive the experimental treatment, allowing researchers to isolate the effect of the independent variable. Scientific controls are a fundamental part of the scientific method, particularly in fields such as biology, chemistry, medicine, and psychology. These complex systems are subject to multiple interacting variables that can distort findings if left unchecked.
Types And Classifications
The simplest types of control are negative and positive controls, and both are found in many different types of experiments. When both succeed, they usually eliminate most potential confounding variables because the experiment produces a negative result when expected and a positive result when expected. Other controls include vehicle controls, sham controls, and comparative controls used across various disciplines. For example, molecular markers used in SDS-PAGE experiments serve specific purposes like ensuring equipment works properly. Control measurements may also be used to subtract background noise from later signal measurements, thus producing a processed signal of higher quality. If a researcher feeds an experimental artificial sweetener to sixty laboratory rats and observes that ten of them subsequently become sick, the underlying cause could be the sweetener itself or something unrelated. To control for the effect of the dilutant, the same test is run twice: once with the artificial sweetener in the dilutant, and another done exactly the same way but using the dilutant alone. Now the experiment is controlled for the dilutant and the experimenter can distinguish between sweetener, dilutant, and non-treatment.
A scientific control is an element of an experiment or observation designed to minimize the influence of variables other than the independent variable under investigation. This process reduces the risk of confounding by ensuring that only the factor being tested changes while all others remain constant.
How do negative and positive controls work together?
Negative and positive controls are the simplest types of control found in many different types of experiments. When both succeed, they usually eliminate most potential confounding variables because the experiment produces a negative result when expected and a positive result when expected.
Why does Lousdal et al use proxies for better health as negative-control outcomes?
Lousdal et al examined the effect of screening participation on death from breast cancer and hypothesized that screening participants are healthier than non-participants. They used proxies for better health as negative-control outcomes to test if exposure was associated with outcome and determine if causally influence exists.
What happens if a study design has no statistical association between NCO and treatment?
If the study design is valid, there should be no statistical association between the Negative Control Outcome and the treatment. An association between them suggests that the design is invalid and the effect of the study on outcome is non-identifiable.
How does randomization correct for systematic errors in experiments?
In randomization, the groups that receive different experimental treatments are determined randomly so that differences are distributed equally. This method mitigates the effect of variations in soil composition on yield or other environmental factors affecting crop yield.
Confounding is a critical issue in observational studies because it can lead to biased or misleading conclusions about relationships between variables. A confounder is an extraneous variable that is related to both the independent variable and the dependent variable, potentially distorting the true association. If confounding is not properly accounted for, researchers might incorrectly attribute an effect to the exposure when it is actually due to another factor. This can result in incorrect policy recommendations, ineffective interventions, or flawed scientific understanding. For example, in a study examining the relationship between physical activity and heart disease, failure to control for diet, a potential confounder, could lead to an overestimation or underestimation of the true effect of exercise. Falsification tests are a robustness-checking technique used in observational studies to assess whether observed associations are likely due to confounding, bias, or model misspecification rather than a true causal effect. These tests help validate findings by applying the same analytical approach to a scenario where no effect is expected.
Negative Control Exposures
Lousdal et al. examined the effect of screening participation on death from breast cancer. They hypothesized that screening participants are healthier than non-participants and therefore already at baseline have a lower risk of breast-cancer death. Therefore, they used proxies for better health as negative-control outcomes and proxies for healthier behavior as negative-control exposures. Death from causes other than breast cancer was taken as NCO, as it is an outcome of better health, not effected by breast cancer screening. Dental care participation was taken to be NCE, as it is assumed to be a good proxy of health attentive behavior. An NCE test will check whether exposure is associated with outcome, and if so then causally influence exists, thus the effect of the study on outcome is non-identifiable. For example, Yerushalmy used husband's smoking as an NCE. The exposure was maternal smoking; the outcomes were various birth factors such as incidence of low birth weight, length of pregnancy, and neonatal mortality rates. It is assumed that husband's smoking share common confounders such household health lifestyle with the pregnant woman's smoking, but it does not causally affect the fetus development. Nonetheless, Yerushalmy found a statistical association, and as a result, it casts doubt on the proposition that cigarette smoking causally interferes with intrauterine development of the fetus.
Negative Control Outcomes
Negative Control Outcomes are the more popular type of negative controls in epidemiology and social sciences fields such as economics. Jackson et al. used mortality from all causes outside of influenza season an NCO in a study examining influenza vaccine's effect on influenza-related deaths. A possible confounding mechanism is health status and lifestyle, such as the people who are more healthy in general also tend to take the influenza vaccine. Jackson et al. found that a preferential receipt of vaccine by relatively healthy seniors leads to bias in estimates of influenza vaccine effectiveness. In a similar example, when discussing the impact of air pollutants on asthma hospital admissions, Sheppard et al. used non-elderly appendicitis hospital admissions as NCO. An NCO test will check whether outcome is associated with treatment, and if so then causally influence exists, thus the effect of the study on outcome is non-identifiable. If the study design is valid, there should be no statistical association between the NCO and the treatment. Thus, an association between them suggests that the design is invalid.
Experimental Design Techniques
In randomization, the groups that receive different experimental treatments are determined randomly. While this does not ensure that there are no differences between the groups, it ensures that the differences are distributed equally, thus correcting for systematic errors. For example, in experiments where crop yield is affected, the experiment can be controlled by assigning the treatments to randomly selected plots of land. This mitigates the effect of variations in soil composition on the yield. Blinding is the practice of withholding information that may bias an experiment. Participants may not know who received an active treatment and who received a placebo. If this information were to become available to trial participants, patients could receive a larger placebo effect, researchers could influence the experiment to meet their expectations, and evaluators could be subject to confirmation bias. A blind can be imposed on any participant of an experiment, including subjects, researchers, technicians, data analysts, and evaluators. In some cases, sham surgery may be necessary to achieve blinding. During the course of an experiment, a participant becomes unblinded if they deduce or otherwise obtain information that has been masked to them. Unblinding that occurs before the conclusion of a study is a source of experimental error as the bias that was eliminated by blinding is re-introduced.