Which bias describes the tendency to use non-representative data leading to biased conclusions?

• A. Selection bias
• B. Availability bias
• C. Confirmation bias
• D. Anchoring bias

Answer: (A)

Selection bias describes the problem that occurs when the data used to draw conclusions come from a subset of the population that isn’t representative. When the way participants or samples are chosen favors certain groups over others, the results reflect those imbalances rather than the true picture for the whole population. In valuation and housing studies, this shows up if you gather data only from properties in one neighborhood or from a group that’s easier to reach. Those samples can distort market estimates, pricing trends, or policy assessments because other areas or groups with different characteristics aren’t adequately represented. This non-representativeness is what makes the conclusions biased. By contrast, availability bias would rely on what’s most easily recalled, confirmation bias on data that confirms a preconception, and anchoring bias on sticking to an initial figure. So the tendency to rely on non-representative data leading to biased conclusions is selection bias.