What are some common mistakes in power analysis?
An example of an AI chatbot conversation about common mistakes in power analysis.
What are some common mistakes in power analysis?
Some common mistakes include using an inadequate sample size, not considering effect size, and ignoring the significance level.
Can you explain why sample size is important?
Sample size affects the power of a study; too small a sample may lead to false negatives, while too large can waste resources.
What is effect size and why does it matter?
Effect size quantifies the strength of a phenomenon; it helps determine how meaningful the results are in practical terms.
How can I avoid these mistakes in my analysis?
To avoid mistakes, ensure you conduct a thorough power analysis before your study, considering all relevant parameters.
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