Which statement best describes non-differential misclassification of exposure?

• A. It biases results toward the null for binary exposure.
• B. It biases results away from the null for binary exposure.
• C. It has no effect on effect estimates.
• D. It only affects continuous exposures.

Answer: (A)

Non-differential misclassification of exposure means the error in classifying someone as exposed or unexposed is similar regardless of whether they developed the outcome. When the exposure is binary, this type of error tends to blur the difference between the exposed and unexposed groups, pulling the observed measure of association toward the null (a relative risk or odds ratio closer to 1). In other words, the true effect is diluted because misclassification affects both groups equally, making them look more alike than they really are. If misclassification were differential—varying by outcome status—the bias could go in either direction, not predictably toward the null. Since the question specifically asks about non-differential misclassification of a binary exposure, bias toward the null is the correct characterization.