What are effect sizes and how do I interpret them?
An example of an AI chatbot conversation about interpreting effect sizes in statistics.
What are effect sizes and how do I interpret them?
Effect sizes are quantitative measures of the magnitude of a phenomenon. They help to understand the strength of relationships or differences in your data.
Can you give me an example of an effect size?
Sure! A common effect size is Cohen's d, which measures the difference between two means. For instance, if the mean score of group A is 50 and group B is 40, Cohen's d would indicate how large that difference is in standard deviation units.
How do I know if an effect size is considered small, medium, or large?
Cohen suggested that a d of 0.2 is small, 0.5 is medium, and 0.8 is large. However, context matters, so consider the field of study.
Are there other types of effect sizes I should know about?
Yes, there are several! Besides Cohen's d, you might encounter Pearson's r for correlation, odds ratios for binary outcomes, and eta-squared for ANOVA.
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