Guide · Recherche sur les peptides
Statistical significance vs practical importance in research
Learn why a threshold-based result does not reveal effect size, precision, clinical relevance, bias, or the full evidence context.
Réponse directe
Réponse directe
Statistical significance addresses how compatible observed data are with a specified statistical model; it does not tell you whether an effect is large, important, unbiased, or likely to apply to an individual. Read the effect estimate, confidence interval, absolute change, outcome relevance, study design, and full evidence body.
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- A significance label is not an effect size.
- Confidence intervals show the precision and range of compatible effects.
- Small effects can cross a threshold in large studies, while important effects can remain uncertain in small studies.
- Practical importance depends on outcomes, tradeoffs, and context.
Read the estimate before the threshold
The point estimate describes the observed magnitude and direction. The confidence interval describes uncertainty around that estimate. A binary significant-or-not label discards much of this information.
Two studies can produce similar estimates while landing on opposite sides of a conventional threshold because their precision differs. That does not make their underlying effects categorically different.
Ask whether the outcome and magnitude matter
A small change in an intermediate biomarker may be statistically clear yet offer uncertain practical value. A patient-important outcome can be more meaningful even when the estimate remains imprecise.
Absolute effects, baseline risk, follow-up duration, adverse events, burden, and alternatives all shape practical interpretation.
Keep design and multiplicity in view
Bias is not erased by a low probability value. Selective outcome reporting, many unplanned analyses, missing data, or an unsuitable comparator can produce a persuasive-looking result.
Check whether the outcome and analysis were prespecified, then compare the result with the wider evidence rather than treating one threshold as a verdict.
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Sources
- 01Cochrane
- 02Cochrane
- 03National Center for Complementary and Integrative Health
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