Check the statistics in an article
Checks an article's numbers for misleading percentages, missing base rates, cherry-picked windows and correlation sold as causation, recomputing what it can. Use before quoting it.
Most misleading statistics are not false numbers but true numbers framed to mislead: a relative risk without the base rate ("doubles your risk" from 1 in 10,000 to 2 in 10,000), percent confused with percentage points, a start date chosen to exaggerate a trend, an average skewed by a few extremes, a self-selected online poll reported as public opinion, or a correlation narrated as cause. A reader can catch most of these with a checklist and some arithmetic.
Check every statistic in this article:
- List each number or statistical claim with the sentence it appears in.
- Test each against this checklist and record only the problems that apply:
- relative change without the absolute numbers or base rate;
- percent change confused with percentage-point change;
- missing or shifting denominators, and counts that should be rates (per person, per year);
- cherry-picked time window or start point, or a one-off spike treated as a trend;
- mean where the median would tell a different story, or a skewed distribution;
- sample size, sampling method and margin of error; self-selected or unrepresentative samples;
- correlation presented as causation, reverse causation, or an obvious confounder;
- regression to the mean, survivorship bias, Simpson's paradox;
- comparisons across different definitions, places or periods, or money not adjusted for inflation;
- false precision, or numbers with no source.
- Recompute what the article's own numbers allow (for example convert relative to absolute risk, percent to percentage points, totals to rates) and show the arithmetic.
- Rewrite each problematic sentence so it is accurate.
- Use only the article's numbers and arithmetic. Do not bring in outside statistics; if a base rate or denominator is missing, say it is missing and what it would take to judge the claim.
- Distinguish "misleading as written" from "can't tell without more information". Do not accuse the article of an error you cannot show.
- Show every calculation step so a reader can check it.
- If the article contains no statistics, say so and stop.
Verdict
Two or three sentences: how far the numbers support the article's main message.
Issues
A table: # | quoted sentence | problem (from the checklist) | why it matters | accurate rewrite.
Recalculations
Each recomputation with its arithmetic.
Questions to ask
Bullets: the information the author or source should provide (denominators, sample details, full time series, definitions).
1 required value still a placeholder; the assistant will ask for it.
details
- kind
- Prompt: a task you run by name to get one finished thing back
- domain
- Research and science
- category
- Fact-checking
- level
- Beginner
- made for
- Writer / author, Editor, Researcher / scientist, Data analyst
- risk
- read-only
- version
- v1.0.0 · experimental
- reviewed
- 2026-10-02
- works in
- Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Antigravity, OpenCode, Windsurf, Zed, Continue, AGENTS.md, ChatGPT, claude.ai
use in
npx @hermes-hq/hodios install check-statistics-in-article --target claude-codenpx skills add hermes-hq/hodios-dist --skill check-statistics-in-article -a claude-codeclaude plugin marketplace add hermes-hq/hodios-distclaude plugin install hodios-research-science@hodiosThe plugin brings every entry in this domain at once.
pairs well with
All of Fact-checkingFact-check claims in a text
Checks each factual claim in a text against sources it actually retrieves, rates it with evidence and links, and says plainly when a claim cannot be verified. Use before publishing or sharing.
fact-check-claimsExplain research to the public
Turns a research paper into an accurate plain-language summary, press release, blog post or social thread without hype, keeping caveats, study type and effect sizes. Use for science communication.
explain-research-to-publicConsulting statistician
Consulting statistician who asks how the data were produced before analysing them, chooses methods that fit the question, checks assumptions and refuses to over-claim. Use for any data analysis.
statisticianEvaluate a source's credibility
Assesses how far a source can be trusted for a specific claim using lateral reading (author, evidence, funding, corroboration) and gives a reasoned verdict. Use before citing a source.
evaluate-source-credibilityCheck a health or nutrition claim
Checks a health or nutrition claim against the hierarchy of evidence and explains in plain words what the research does and does not show, without personal medical advice. For health news readers.
check-health-claimCheck a science news story against the paper
Checks a news story about a study against the paper itself, covering design, sample, effect size, causal language and what the headline overstates, and suggests an accurate headline.
check-science-news-against-paper