
How peer review works, and what it doesn't catch
Peer review is a check by outside experts on a study's methods and reasoning before publication — a filter that improves papers but cannot verify data, replicate results, or catch deliberate fraud.
Literacy teaches the skills for evaluating evidence directly: distinguishing correlation from cause, reading confidence intervals, converting relative risk into absolute terms, spotting undeclared conflicts, and recognizing when a sample is too small. Examples come from real published papers. For anyone wanting to check a health claim independently.
Literacy teaches the skills for evaluating evidence directly: distinguishing correlation from cause, reading confidence intervals, converting relative risk into absolute terms, spotting undeclared conflicts, and recognizing when a sample is too small. Examples come from real published papers. For anyone wanting to check a health claim independently.

Peer review is a check by outside experts on a study's methods and reasoning before publication — a filter that improves papers but cannot verify data, replicate results, or catch deliberate fraud.

A 2025 analysis in PNAS finds reviewers often disagree with each other — and shows which specific fixes actually help.

A five-step checklist — source, study, design, size, conflicts — separates checkable findings from marketing wearing science's clothes.

Writing down the plan before the data arrive — outcomes, sample size, analysis — forecloses the quiet ways results get shaped afterward.

Exaggeration often enters the pipeline before any journalist writes a word — in the university's own announcement.

Science is cumulative by design: single findings are provisional by default, and the checks that confirm or retire them take time.

Fake peer review leaves fingerprints: promises of days-fast review, unlisted editors, and fees that surface only after acceptance.

The range around a study's headline number — not the number itself — shows how much the result could plausibly move.

The same finding can be reported as a 50 percent increase or as a few extra cases per thousand — both true, only one honest without the other.

A single vivid story carries a hidden sample of one — and often a sample that survived a filter the listener never sees.