To determine statistical results, you must evaluate the p-value for statistical significance, calculate the effect size to measure the magnitude of the finding, and review confidence intervals to assess the precision of your data.
Interpreting the output from statistical software (like SPSS, R, or Python) goes beyond just looking for a "winning" number. To accurately understand what your data is telling you about your research question, you need to look at several metrics together.
1. Check for Statistical Significance (p-values)
Begin by looking at the p-value to test your null hypothesis. The p-value tells you the probability that your observed results occurred by random chance. In most academic disciplines, an alpha level of 0.05 is the standard threshold. If your p-value is less than 0.05, your results are considered statistically significant. However, a low p-value only confirms that an effect likely exists—it does not tell you whether that effect is actually meaningful in the real world.
2. Evaluate the Practical Significance (Effect Size)
While statistical significance tells you an effect exists, the effect size tells you how large and important it is. Metrics like Cohen's d (for comparing groups), Pearson's r (for correlations), or odds ratios measure the actual magnitude of your findings. This is a critical step because a study with a massive sample size might produce a statistically significant p-value for a tiny, practically irrelevant effect. Always report effect sizes alongside your p-values.
3. Review Confidence Intervals (CIs)
A 95% confidence interval provides a range of values within which you can be 95% certain the true population parameter lies. CIs help you determine the reliability of your statistical results. Narrow confidence intervals suggest high precision in your data, while wide intervals indicate a high level of uncertainty. Additionally, if your confidence interval crosses zero (when testing for a difference), the result is typically not statistically significant.
4. Contextualize the Findings
Finally, you must compare your statistical output against existing literature in your field. If you are reading related studies and struggling to understand how other authors interpreted similar statistical models, using WisPaper's Scholar QA allows you to ask questions about a paper's results and traces the answers back to the exact page and paragraph to help you verify complex claims. Ultimately, statistical results should never be determined in a vacuum; they must be interpreted within the context of your specific research design and methodology.
