Guide · Recherche sur les peptides
Sample size and study power: why small studies mislead
Understand precision, events, variability, statistical power, and why a small positive or negative study rarely settles a peptide claim.
Réponse directe
Réponse directe
Sample size affects how precisely a study can estimate an effect and whether it can reliably detect a prespecified difference. Small studies often produce wide confidence intervals and unstable estimates. A non-significant small study may be inconclusive, while a surprising positive result may be exaggerated; design quality and event counts still matter.
Ce qui compte
- Participant count is only one part of information size; event frequency and variability matter too.
- Wide confidence intervals should be read as uncertainty, not proof of no effect.
- Small positive studies are especially vulnerable to exaggerated estimates and selective visibility.
- A power calculation should be tied to a prespecified meaningful difference.
Sample size controls precision
Larger studies usually estimate effects more precisely, producing narrower confidence intervals, but the number of events and variability in the outcome also matter. A large enrollment with few relevant events can remain uninformative.
Precision does not guarantee validity. A very large biased study can estimate the wrong quantity with great apparent confidence.
Power is a design property, not a rescue argument
Before a study begins, power calculations connect expected variability, a chosen error rate, the effect the study aims to detect, and required information size. The meaningful difference should be justified, not selected to make enrollment convenient.
After results exist, the observed effect and confidence interval are more informative than a retrospective claim that the study was underpowered.
Interpret small peptide studies cautiously
Early peptide studies may be useful for feasibility, pharmacology, or signal detection, but small samples rarely settle effectiveness or uncommon harms. Replication in independent, appropriate populations matters.
When confidence intervals include both meaningful benefit and meaningful harm, the honest conclusion is uncertainty—not whichever side the headline prefers.
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Sources
- 01Cochrane
- 02Cochrane
- 03National Center for Complementary and Integrative Health
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