your primary hypothesis before looking at the data.
Use or similar adjustments if testing multiple variables. 3. The Power of "No" A non-significant result (
) 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. Wise Use of Null Hypothesis Tests: A Practition...
Always report (e.g., Cohen’s d or Pearson’s r) to show the magnitude of the finding. 2. Stop the "P-Hacking"
A 95% CI tells you the likely neighborhood of the true effect. your primary hypothesis before looking at the data
Conduct a before your study to ensure your sample size is large enough.
If the CI is very wide, your estimate is precise regardless of the p-value. Summary Checklist for Practitioners The Power of "No" A non-significant result (
P-values are a "Yes/No" toggle. Confidence Intervals (CIs) provide a .