Confounding Variable vs Response Bias
Both terms below come up in the same part of the course, and students mix them up. Here is each one defined on its own, side by side, so you can see where they part company.
Confounding variable
Collecting data and study design
A confounding variable is one whose effect on the response is tangled with the explanatory variable's, so you cannot separate their two influences.
A confounding variable changes along with the explanatory variable, so its effect and the explanatory variable's effect on the response cannot be told apart. This is why an observed association does not by itself prove causation. For example, if students who attend tutoring also study more, and tutored students score higher, study time confounds the effect of tutoring. Random assignment defends against confounding by spreading such variables evenly across the treatment groups on average.
Response bias
Collecting data and study design
Response bias occurs when respondents give systematically inaccurate answers, for example due to confusing question wording or pressure to answer a certain way.
Response bias is about the accuracy of the answers people give, not about who is included in the sample. For example, a leading question such as asking whether people support a helpful new policy nudges respondents toward agreement. Sensitive topics can also cause it, since people may understate behaviors they think are frowned upon. Careful, neutral question wording and anonymity help reduce response bias.