Why must researchers control for confounding variables?

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BlackTom AI · Accepted Answer

The question asks: Why must researchers control for confounding variables?

Option provided: 'To ensure the relationship between variables is not spurious.' This is the core reason. By holding potential confounders constant or accounting for them in analysis, researchers can determine whether an observed association reflects a true relationship rather than being produced by an unmeasured third variable. In other words, controlling for confounds helps establish internal validity and reduces bias in causal inference.

If we consider why this matters, suppose two variables A and B appear related. Without controlling for a confounder C that influences both A and B, the observed link could be merely due to C, making the A–B relationship spurious. Controlling for C helps us ask: would A and B still be related if C were held constant?

Given that no alternative options are provided in this dataset, we focus on the stated option and its logic. In typical exams, incorrect choices might suggest that confounds are irrelevant, that they only affect external validity, or that they can be ignored in observational studies, all of which are misleading. The correct framing emphasizes the need to rule out spurious associations to draw valid conclusions about relationships between variables.

Overall, the primary purpose of controlling for confounding variables is to prevent spurious conclusions and to strengthen the credibility of any inferred relationship between the studied variables.

2261 PS 1340 SEC1000 Quiz 1

Why must researchers control for confounding variables?

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