Which statement about fairness and normative samples is correct?

Which statement about fairness and normative samples is correct?

• A. Fairness is not a concern in norming.
• B. Normative samples should be large.
• C. Fairness has no impact on decision making.
• D. Normative samples should reflect the target population to ensure fair and meaningful interpretation across groups.

Answer: (D)

When we interpret test scores, we rely on a reference group that represents the people the test will be used with. If the normative sample reflects the target population, the resulting norms (like percentiles and standard scores) provide fair and meaningful interpretations across different groups. This helps ensure that scores mean the same thing no matter who is taking the test, so decisions based on those scores are just and valid for everyone. If the normative sample is biased or unrepresentative, some groups can be advantaged or disadvantaged by the norms themselves, leading to unfair conclusions and misinterpretations.

Choosing a very large sample size helps reduce sampling error, but it doesn’t guarantee fairness on its own; representativeness is the key for equitable interpretation. Fairness clearly matters in norming because biased norms can drive biased decisions. And fairness does influence decision making—if the norms aren’t fair, decisions informed by them won’t be fair either.

Therefore, normative samples should reflect the target population to ensure fair and meaningful interpretation across groups.