Explain bias and confounding in research and give an example of each.

• A. Bias is a random error in data collection.
• B. Bias is a systematic error in data collection or analysis; confounding is an extraneous variable that correlates with both the IV and DV.
• C. Confounding is the same as bias.
• D. Bias and confounding are unrelated concepts.

Answer: (B)

Bias and confounding threaten study validity by distorting observed relationships. Bias is a systematic error in data collection or analysis that pushes measurements away from the truth in a consistent direction. For example, using a survey with leading questions can overstate how often people engage in a behavior, or using a faulty instrument that consistently reads high can bias measurements upward. Confounding, on the other hand, happens when an extraneous variable is related to both the exposure (the independent variable) and the outcome (the dependent variable), creating a false impression of a direct link. A classic example is examining whether coffee drinking is linked to heart disease; if age is not accounted for, older people (who drink more coffee) also have a higher baseline risk for heart disease, making coffee appear related to heart disease even if it isn’t causal. To address bias, researchers strive for standardized methods, blinding, and objective measurements; to address confounding, they use randomization, matching, stratification, or statistical adjustment.