P-values are a "Yes/No" toggle. Confidence Intervals (CIs) provide a .
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...
If you run twenty different tests on the same data, one will likely be significant just by chance. P-values are a "Yes/No" toggle
Did I avoid "fishing" for significance by running dozens of tests? 💡 Practical Rules for Wise Use 1
) is the baseline assumption that . No effect, no difference, no change. The goal of the test isn't to prove your idea is right, but to see if your data is "weird" enough to make the H0cap H sub 0 look unlikely.
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.
your primary hypothesis before looking at the data.