Overview
Statistical significance interpretation fails when p-values are treated as measures of effect magnitude. A p-value of 0.001 does not mean the effect is large — it means the effect is unlikely to be due to chance. A study can find a statistically significant effect that is too small to matter practically, or a non-significant effect that is large but underpowered. The p-value alone does not tell you whether the finding is important.
The Statistical Significance Interpretation Framework Prompt builds an interpretation protocol that combines p-values with effect sizes and confidence intervals — reporting what the data actually show rather than what the p-value seems to suggest, distinguishing statistical significance from practical importance, and avoiding the interpretation errors that lead to overstated conclusions.
What you get: - P-value interpretation rules: what p-values mean and what they do not mean - Effect size calculation: the metrics that measure magnitude of effects - Confidence interval interpretation: how to read and report CIs correctly - Statistical vs. practical significance: the framework that distinguishes them - Multiple comparison correction: when and how to adjust for multiple tests - Reporting standards: the format that communicates findings without overstatement
Built for: researchers interpreting statistical analyses in quantitative research across all disciplines.