To ensure data comparisons across schools are fair, what preparation step should be performed?

• A. Normalize year-to-year poverty metrics and assign weights to schools.
• B. Compare schools using only raw average scores.
• C. Ensure data governance includes privacy protections; avoid biased interpretations that harm subgroups.
• D. Publish all data publicly.

Answer: (C)

Fair data comparisons hinge on responsible data governance and thoughtful interpretation. Establishing data governance that includes privacy protections ensures student information is handled securely and de-identified when appropriate, shielding identities and reducing privacy risks. It also creates guardrails for how results are reported, so subgroup findings aren’t misinterpreted or used to stigmatize students in ways that would unfairly harm certain groups. This foundation helps ensure comparisons across schools are fair, accurate, and trustworthy.

Other options miss this protective and interpretive layer. Relying on raw averages ignores context and sample differences; technical tweaks like normalizing metrics or weighting can influence results but don’t address privacy or biased interpretations. Publishing all data publicly can violate privacy and invite misuse, rather than safeguarding fairness.