Wise Use Of Null Hypothesis Tests: A Practition... -

) does not mean there is no effect; it might just mean your study was too small to find it.

Usually set at 0.05, meaning we accept a 5% risk of being wrong when we claim an effect exists. 💡 Practical Rules for Wise Use 1. Significance ≠is not equal to Importance Wise Use of Null Hypothesis Tests: A Practition...

With a massive sample size, even a tiny, useless difference will be "statistically significant." ) does not mean there is no effect;

If you tell me the you're working in (e.g., medicine, marketing, psychology), I can: Suggest the best specific tests for your data types. Provide a template for reporting results to stakeholders. Explain how to handle non-normal data distributions. Significance ≠is not equal to Importance With a

Conduct a before your study to ensure your sample size is large enough.

Did I check my data for outliers and normality before testing? Is my sample size justified by a power calculation? Am I reporting the effect size alongside the p-value?

Interpret high p-values as "inconclusive" rather than "proof of zero effect." 4. Contextualize with Confidence Intervals