p-values and effect sizes answer different questions. p-value tells you if an effect exists. Effect size tells you how large or meaningful that effect is. Both are essential for complete statistical reporting.
Interpretation guidelines and common thresholds in academic research
p = 0.03 means 3% probability that results occurred by chance.
p-value is probability of observing data IF null hypothesis is true, not probability null hypothesis is true.
p < 0.05 means the effect is practically important or large.
Significance does not equal importance. Effect size measures magnitude.
p > 0.05 proves the null hypothesis is true.
Failure to reject null does not confirm it. Could be small sample or low power.
Effect size quantifies the magnitude of an effect, independent of sample size
A statistically significant result (p < 0.05) can still be trivial if the effect size is tiny. Conversely, a non-significant result may hide a meaningful effect if sample size is small. Always report and interpret effect sizes alongside p-values.
APA 7th edition guidelines for reporting statistical results
Perform appropriate test (t-test, ANOVA, correlation, regression) using software (SPSS, R, JASP).
Write "p = .032" not "p < 0.05" (except for p < .001). Include test statistic and degrees of freedom.
Include Cohen's d, η², or r. Interpret magnitude using field-specific benchmarks.
Include 95% CI for effect sizes (e.g., d [95% CI: 0.32, 0.68]).
t(58) = 2.34, p = .023, d = 0.61 [95% CI: 0.28, 0.94]
F(2, 87) = 4.56, p = .013, η² = 0.09
r(112) = .42, p < .001, 95% CI [.25, .56]
χ²(1, N = 150) = 6.83, p = .009, φ = 0.21