So, your study found a statistically significant result. Great! But what does that *really* mean? Enter: effect size. This isn't just about p-values; it's about the *magnitude* of your finding. Think of it like this: significance tells you if an effect exists, while effect size tells you *how big* that effect is.
Common effect size measures include Cohen's d (for comparing means) and Pearson's r (for correlations). A larger Cohen's d, for instance, indicates a bigger difference between the groups you're comparing. Understanding effect size allows you to assess the practical importance of your research. Is the intervention actually making a meaningful difference in people's lives, or is the effect so small it's negligible? Don't get lost in the p-value weeds – embrace the power of effect size to tell the complete story!
