confound

Definition:
A confound, or confounding variable, is an extraneous variable that influences both the independent variable (IV) and the dependent variable (DV) in a research study. This can create a misleading association between the IV and DV, suggesting a relationship that may not actually exist.

Requirements for a Variable to be a Confounder

As a general rule confounds are a bigger concern for non-experimental studies, precisely because they’re not proper experiments: by definition, you’re leaving lots of things uncontrolled, so there’s a lot of scope for confounds working their way into your study. Experimental research tends to be much less vulnerable to confounds: the more control you have over what happens during the study, the more you can prevent confounds from appearing. (source)

However when we start thinking about artifacts rather than confounds, the shoe is very firmly on the other foot. For the most part, artifactual results tend to be a concern for experimental studies than for non-experimental studies.

To see this, it helps to realise that the reason that a lot of studies are non-experimental is precisely because what the researcher is trying to do is examine human behaviour in a more naturalistic context. By working in a more real-world context, you lose experimental control (making yourself vulnerable to confounds) but because you tend to be studying human psychology “in the wild” you reduce the chances of getting an artifactual result.

Example of a Confound
Consider a study examining the relationship between ice cream consumption and sunburn rates. Higher ice cream consumption correlates with more sunburns. However, the confounding variable here is temperature, which affects both ice cream consumption and sun exposure. (Google Gemini, 2025)

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