Which practice best avoids bias in data analysis?

• A. Rely on a single dataset and skip preregistration
• B. Use diverse data, pre-register analyses, blind review, peer checks, and transparent reporting
• C. Repeat the same analysis multiple times until it matches expectations
• D. Ignore peer review

Answer: (B)

Bias in data analysis happens when decisions about what to test, how to test it, or how to report results are influenced by what researchers expect to find. The strongest way to avoid this is to combine several safeguards that address different bias sources. Using diverse data reduces the risk of drawing conclusions from a single sample and improves generalizability, preventing results from being driven by idiosyncrasies of one dataset. Preregistration locks in hypotheses and analysis plans before seeing the results, blocking data-dredging and designing analyses to produce desired outcomes after the fact. Blind review and analysis helps prevent researchers’ expectations from shaping interpretations or choices of methods. Peer checks bring independent scrutiny that can catch mistakes or biased reasoning that slip past the original team. Transparent reporting ensures methods, data, and code are open to verification and replication, discouraging selective reporting and enabling others to reproduce findings.

In contrast, relying on a single dataset, chasing significant results until they fit expectations, or skipping external validation lacks these safeguards and makes bias more likely.