How to Interpret P-Values and Confidence Intervals
2026-05-04 • 7 min
Quick Answer
P-values test compatibility between observed data and a null model; they do not measure effect importance. Confidence intervals estimate plausible effect ranges and precision. Strong interpretation combines both metrics with study quality, assumptions, and practical impact to avoid misleading conclusions.
What P-Values Do and Do Not Mean
A p-value is not the probability that the null hypothesis is true. It quantifies how unusual observed data are under model assumptions.
Confidence Intervals Add Context
Intervals reveal effect magnitude and uncertainty, helping distinguish statistically significant but clinically trivial findings.
Decision Quality
Interpret statistical evidence with prior plausibility, design quality, and external validity rather than threshold-only logic.
Frequently Asked Questions
Can a non-significant result still be useful?
Yes. It can inform uncertainty, detect small effects, and guide better-powered follow-up studies.
Why report effect size with p-values?
Effect sizes indicate practical importance, while p-values alone only address statistical compatibility.