When a confound is present, what is the effect on the observed IV–DV relationship?

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Multiple Choice

When a confound is present, what is the effect on the observed IV–DV relationship?

Explanation:
Confounding occurs when a third variable is related to both the predictor and the outcome, biasing the observed relationship between them. When a confound is present, it distorts the apparent effect of the independent variable on the dependent variable because part of what looks like an effect of the IV is actually due to the confounding variable. This can make the IV seem stronger or weaker than it truly is, or even suggest a relationship that isn’t causal. If you control for the confound—through design choices like randomization or matching, or through statistical adjustment—the true association between the IV and DV becomes clearer and less biased. The other statements aren’t accurate: a confound isn’t defined by the IV causing it, nor is it determined by the DV alone, and a confound by definition relates to both the IV and the DV.

Confounding occurs when a third variable is related to both the predictor and the outcome, biasing the observed relationship between them. When a confound is present, it distorts the apparent effect of the independent variable on the dependent variable because part of what looks like an effect of the IV is actually due to the confounding variable. This can make the IV seem stronger or weaker than it truly is, or even suggest a relationship that isn’t causal. If you control for the confound—through design choices like randomization or matching, or through statistical adjustment—the true association between the IV and DV becomes clearer and less biased. The other statements aren’t accurate: a confound isn’t defined by the IV causing it, nor is it determined by the DV alone, and a confound by definition relates to both the IV and the DV.

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