Effect size, uncertainty and statistical significance
A tiny effect can have a small p-value; a useful effect can have wide uncertainty. Learn to read magnitude, confidence interval and clinical relevance rather than worshipping p < 0.05.
What p-values do not say
Under a specified statistical model, a p-value describes how incompatible the observed data are with a particular null hypothesis. It is not the probability that the product works, that the hypothesis is true, or that the improvement matters to a user. Multiple tested outcomes increase the risk of isolated 'significant' findings.
Effect size, uncertainty and statistical significance — a published visual reference for the topic, not SESH product-test data.
Open full-size image ↗Absolute effects beat impressive percentages
If a serum changes a redness score by 0.2 points on a 100-point scale, '20% better than control' may still reflect a negligible difference. Ask for the original baseline, absolute between-group difference and established minimally important difference when available. Cosmetic claims should not transform statistical detectability into guaranteed visible change.
Confidence intervals are ranges of compatible effects
A 95% confidence interval is a procedure that, under assumptions, yields intervals covering the fixed effect across repeated samples 95% of the time. It is not a 95% probability statement about the true effect for this one interval. Wide intervals mean the study is compatible with multiple effect sizes. If the range includes no benefit and meaningful benefit, conclusions should remain cautious.
The chart is educational, not cosmetic evidence
الصورة المرخصة بتشرح فكرة فترات الثقة، ولا تخص تجربة SESH أو مرضًا جلديًا. Use plots to see estimates and intervals together, then return to real skincare data before suggesting a product.
These curves show how changing mean and variance affects a statistical model. They are theoretical teaching data, not skin or fragrance-panel results.
Open full-size image ↗Evidence lab · hypothetical data
Hypothetical: Product A has p=0.01 but improves a 100-point scale by 0.3 points; Product B improves by 8 points, but its interval ranges from −2 to +18. Which result is more certain, and which improvement might matter more?
Defensible interpretation
A may have a more statistically detectable but tiny effect. B's estimated magnitude may matter more but is imprecise and compatible with no benefit. Neither earns a confident consumer promise without context.
Research references
Related ingredient
niacinamide · SESH Ingredient DictionaryThe ingredient link is educational, not efficacy proof.
Educational material, not medical advice or a product promise.
