Nightingale Psychology Practice Exam

Effect size is about the magnitude of the difference or the strength of a relationship, which tells you how big the observed effect is in practical terms. This matters alongside p-values because statistical significance alone doesn’t tell you how important the finding is in real-world terms. A result can be statistically significant with a very small but detectable effect if the sample is large, or it can be non-significant with a substantial effect if the sample is small. Reporting the effect size helps you judge practical significance, compare results across studies, plan future research with power analyses, and synthesize evidence in meta-analyses. Common measures include Cohen’s d for differences between groups, correlation coefficients like r for relationships, and odds ratios for binary outcomes. The p-value, by contrast, indicates how unlikely the observed data would be if there were no true effect, not how big that effect is. It does not convey magnitude. It also isn’t a measure of the probability that the null hypothesis is true, which is a different interpretation often confused with p-values. And it doesn’t reflect the rate of Type I error—that's set by the alpha level and relates to the frequency of false positives over many tests, not to how large an effect is.