Linking form-component adoption to fewer a11y bugs

WHAT THIS TESTS This evaluates analytical rigor: can you design a measurement that supports the claim without overstating causality.

A GOOD ANSWER COVERS Define both variables precisely. Adoption is the proportion of forms or form fields built from system components, measured per team, repo, or surface via code scanning. The outcome is accessibility-related bug reports, normalized by a denominator like number of forms, sessions, or screens so larger products are not unfairly weighted. Then design the comparison. A cross-sectional view compares high-adoption versus low-adoption teams; a longitudinal view tracks each team before and after they adopt. Strengthen causal inference with a staged or phased rollout and a difference-in-differences analysis, comparing adopters against not-yet-adopters over the same period. Control for confounders: team size, traffic, codebase age, and whether reporting practices changed. State honestly that correlation is not causation, and note threats like teams that adopt also being more accessibility-mature to begin with. Report effect size and confidence, not just direction.

COMMON WRONG ANSWERS Claiming causation from one aggregate before-and-after number. Ignoring confounders. Not normalizing bug counts. Cherry-picking the best team. Confusing fewer reports with fewer actual defects.

LIKELY FOLLOW-UPS What confounders worry you most? How would a phased rollout sharpen causal claims? Why normalize the bug count?

ONE CONCRETE EXAMPLE You measure form-component adoption per team and normalize accessibility bugs per 100 form sessions. Comparing teams above 70 percent adoption against those below 30 percent, the high group reports about 40 percent fewer accessibility bugs. A staged rollout with difference-in-differences confirms the drop tracks the adoption timing, while you control for team size and traffic.