Effect size

The effect size is how large a difference actually was — separate from whether it was statistically significant, which only tells you the data were inconsistent with no difference under the test’s assumptions. In peptide research, this is the difference between “the compound changed something” and “the change was large enough to be useful.”

What does effect size mean?

Effect size means the magnitude of a result: how far apart two groups were, how much a risk changed, or how strongly two variables moved together. The number may be a mean difference, risk ratio, odds ratio, or standardized measure such as Cohen’s d. Outcomes need different rulers.

The Cochrane Handbook distinguishes difference measures, where zero means no difference, from ratio measures, where one means no difference.

Is statistically significant the same as meaningful?

Statistically significant and meaningful are not synonyms. A p-value helps judge whether a result is compatible with a no-effect model; effect size tells you how much the groups differed. Large studies can detect a small effect size with great confidence, even when the change cannot justify the cost or side effects.

The statistically significant vs meaningful question depends on context. A clinically meaningful difference is large enough to matter for symptoms, function, risk, or quality of life. “P < 0.05” cannot settle that judgment. The effect-size primer explains why magnitude and statistical testing belong.

How does effect size expose peptide hype?

Effect size exposes peptide hype by forcing the claim to match the outcome that moved. A biomarker change is not symptom relief; added fat-free mass is not automatically stronger muscle. Our guide to reading peptide evidence applies that check across the growth-hormone hub.

In a 65-person randomized controlled trial, fat-free mass rose 1.1 kg with MK-677 and fell 0.5 kg with placebo after one year: a 1.6 kg difference, with P < 0.001. Yet strength and physical function did not improve. The body-composition result was real; the useful performance result was absent. “Statistically significant” becomes a sales pitch when nobody asks what changed.

Is a small effect size always unimportant?

A small effect size is not automatically unimportant. Meaning depends on the outcome, baseline risk, uncertainty, safety, cost, and alternatives. A modest reduction in a serious event may matter; a tiny shift in a surrogate lab marker may not.

The practical effect size meaning is simple: ask “how much?” immediately after “did it work?” Then check the confidence interval and measured endpoint.