To interpret statistical results effectively, you must evaluate the p-value, effect size, and confidence intervals to determine both the statistical and practical significance of the findings.
While statistical analysis software will give you a wealth of numbers, translating that data output into a meaningful conclusion requires a structured approach. Many early-career researchers make the mistake of stopping at the p-value, but a complete interpretation requires looking at the broader picture.
Here is a step-by-step guide to making sense of statistical results in your own data or in a published research paper.
1. Check the P-Value for Statistical Significance
The p-value helps you test your null hypothesis. Traditionally, a p-value of less than 0.05 indicates statistical significance, meaning there is less than a 5% probability that your results occurred purely by random chance. However, a low p-value only tells you that an effect likely exists—it does not tell you if that effect is actually important.
2. Measure Magnitude with Effect Size
To understand the real-world impact or practical significance of your findings, you need to look at the effect size. Common metrics include Cohen’s d for differences between groups or Pearson’s r for correlations. Effect size tells you how large the difference or relationship actually is. This is a crucial metric because, with a massive sample size, even a microscopic and meaningless difference can produce a statistically significant p-value.
3. Assess Precision Using Confidence Intervals
A 95% confidence interval (CI) provides a range of values within which the true population parameter likely falls. If your confidence interval is narrow, your results are highly precise. If it is incredibly wide, your data has a lot of variance and uncertainty. Confidence intervals provide much more context than a simple "yes or no" p-value because they show the best- and worst-case scenarios for your data.
4. Contextualize Within the Literature
Finally, statistical results must be interpreted within the context of your specific research question and existing literature. Numbers do not speak for themselves; you have to explain what they mean for your field. If you are struggling to decipher the dense methodology or results sections of related literature to compare your findings, you can use WisPaper's Scholar QA to ask direct questions about a paper, which helps you understand complex statistical claims by tracing the answers back to the exact page and paragraph.
By combining p-values, effect sizes, and confidence intervals, you will be able to write a discussion section that accurately and confidently reflects the true weight of your research data.
