A transit satisfaction survey shows rising scores, but unhappy riders appear more likely to abandon the survey mid-way. What missingness concern should the analyst raise?
Select an answer to reveal the explanation.
Short Explanation
If unhappy riders bail mid-survey, the remaining smiles are a skewed sample—like reviewing a restaurant using only people who finished dessert. Ask who is missing before celebrating the average. Missingness can be the bias engine.
Full Explanation
Non-random missingness related to the outcome can bias estimates; here, dropout by dissatisfied riders can inflate apparent satisfaction. Analysts should examine who is missing and how missingness relates to key measures before interpreting trends. Ignoring missingness, treating only demographic gaps as relevant, or imputing maximum scores by default worsens distortion. Bias awareness is part of sound analysis fundamentals.