To verify statistical results in an academic paper, you must critically evaluate the sample size, compare the reported p-values against the effect sizes, and confirm that the chosen statistical tests match the study's research design.
Understanding how to interpret and verify data is an essential skill for any researcher conducting a literature review or designing a new study. Here are the most effective steps to ensure the statistics you are reading are reliable.
1. Evaluate Sample Size and Statistical Power
Before trusting a significant result, look closely at the number of participants or observations (N). Small sample sizes often lead to underpowered studies, which increases the risk of false positives or inflated effects. Check if the authors conducted an a priori power analysis to mathematically justify their sample size before data collection began.
2. Look Beyond the P-Value
While a p-value of less than 0.05 indicates statistical significance, it does not measure the actual magnitude or importance of the findings. Always check the reported effect sizes (such as Cohen's d, Pearson's r, or odds ratios) alongside confidence intervals. A highly significant p-value paired with a tiny effect size might not be practically meaningful in a real-world context.
3. Cross-Check the Methodology and Assumptions
Every statistical test—whether an ANOVA, t-test, or multiple regression—relies on specific underlying assumptions like normal distribution, independent variables, or equal variance. Review the methodology section to confirm the researchers tested for these assumptions before running their analysis. If you are struggling to parse dense methods sections, you can use WisPaper's Scholar QA to ask direct questions about a study's statistical choices and trace the answers back to the exact page and paragraph.
4. Watch Out for P-Hacking
Be cautious of "p-hacking," a practice where researchers run multiple analyses but only report the ones that yield statistically significant results. Warning signs include unexpectedly complex data transformations, dropping outliers without a clear, pre-registered justification, or reporting an unusual cluster of p-values resting just below the 0.05 threshold (such as 0.049).
5. Check for Open Data and Reproducibility
The gold standard for verifying statistical claims is replication. Look for a data availability statement to see if the authors uploaded their work to public repositories like OSF or GitHub. Accessing the raw datasets and analysis scripts allows you to re-run the numbers yourself, ensuring the reported findings are completely accurate and free from computational errors.
