results-interpretation

Effect size interpretation emphasizes practical significance over p-values alone. Report the effect size (e.g., Cohen's d) alongside its 95% confidence interval to convey both magnitude and precision. A p-value indicates statistical significance, but effect size and CI reveal whether the result is meaningful in practice.

A confidence interval expresses the range of plausible values for your estimate and reflects measurement precision. A narrow 95% CI indicates high precision; a wide CI suggests uncertainty. Interpret CIs to discuss practical implications rather than relying solely on point estimates.

Report the test statistic with degrees of freedom, the p-value, the effect size, and its 95% confidence interval. This combination satisfies standard reporting requirements for descriptive statistics and conveys both statistical and practical significance, avoiding misinterpretation.

Label analyses planned before data collection as primary; analyses conducted after observing the data are exploratory. Report exploratory findings separately and with caution, as multiple comparisons increase false positive risk without adjustment.

A p-value does not measure the probability that your hypothesis is true or the size of an effect. It only indicates whether observed data are extreme under the null hypothesis. Emphasize effect size and confidence intervals to prevent overstatement of weak results.