What are effect sizes in statistical testing?

What are effect sizes in statistical testing?

Effect sizes measure the strength of a relationship or the magnitude of an effect in statistical testing. They help to understand the practical significance of results.

Why are effect sizes important?

Effect sizes provide context to p-values, indicating whether a statistically significant result is also meaningful in real-world terms.

Can you give me examples of different types of effect sizes?

Sure! Common types include Cohen's d for comparing means, Pearson's r for correlation, and odds ratios for categorical data.

How do I calculate effect size?

The calculation depends on the type of effect size. For Cohen's d, you subtract the means of two groups and divide by the pooled standard deviation.

What is a small, medium, and large effect size?

Cohen suggested that a small effect size is around 0.2, medium is 0.5, and large is 0.8, but context matters!