What practice helps prevent measurement bias in data collection?

• A. Exclude Reliable Data
• B. Use Imputation To Fill Gaps
• C. Ignore Data Quality
• D. Exclude Unreliable Or Missing Data

Answer: (D)

Measurement bias comes from flaws in how data are collected, causing measurements to skew systematically. The most effective way to prevent this is to ensure data quality by excluding data that are unreliable or missing. Unreliable measurements come from faulty instruments, inconsistent procedures, or observer errors, and including them can pull results away from the true values. Missing data pose a similar risk because their absence can bias estimates if not handled properly. While imputation can address gaps, it relies on assumptions about why data are missing and how they should look, which can itself introduce bias. By focusing on data that are clearly reliable and complete, you minimize the chances of measurement bias influencing your findings.