What are the common mistakes in calculating effect sizes?
An example of an AI chatbot conversation about common mistakes in calculating effect sizes.
What are the common mistakes in calculating effect sizes?
Some common mistakes include not considering sample size, misinterpreting the effect size, and using inappropriate formulas.
Can you explain why sample size is important in this context?
Sample size affects the precision of the effect size estimate; smaller samples can lead to unreliable results.
What are some inappropriate formulas that people might use?
Using formulas meant for different types of data or analyses, like using Cohen's d for non-parametric data, is a common error.
How can I avoid these mistakes in my research?
Double-check your formulas, ensure your sample size is adequate, and consult statistical guidelines or experts.
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