Reporting Results: p-values, Effect Sizes and APA
How to write up your statistics so they are correct, clear and in APA style, the part of the methods and results that markers scrutinise most.
What a p-value is, and is not
A p-value is the probability of obtaining your result, or a more extreme one, if the null hypothesis were true. It is not the probability that your hypothesis is correct, and it is not a measure of effect size. Treat .05 as a convention, not a magic threshold, and never write "approaching significance".
Always pair significance with an effect size
Significance tells you whether an effect is likely to be real; effect size tells you how big it is. Report both. Common effect sizes are Cohen’s d for differences between means, eta squared or partial eta squared for ANOVA, and r for correlations.
APA reporting formats
Markers reward precise, conventional reporting. Italicise test statistics, report exact p-values to three decimal places (or p < .001), and include degrees of freedom.
- — t-test: t(48) = 2.13, p = .038, d = 0.61.
- — ANOVA: F(2, 87) = 5.42, p = .006, η² = .11.
- — Correlation: r(58) = .34, p = .009.
- — Regression: report B, SE, β, t and p for each predictor, plus R² for the model.
Tables, figures and confidence intervals
Put detailed numbers in a clearly labelled table rather than crowding the prose, and report 95% confidence intervals wherever you can. A confidence interval is often more informative than a p-value because it shows both the size and the precision of your estimate.
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