statistics-verifier
Statistics Verifier provides structured checklists and frameworks for validating statistical claims, assessing research methodology, and identifying analytical errors. It covers claim verification protocols, red flags in reporting, common statistical pitfalls, significance testing guidance, and causation assessment criteria to help you audit data analysis and fact-check research findings.
Statistics Verifier helps you validate statistical claims and research methodology through structured verification frameworks.
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Decision gist · record as of 2026-07-17
Statistics Verifier helps you validate statistical claims and research methodology through structured verification frameworks. Statistics Verifier provides structured checklists and frameworks for validating statistical claims, assessing research methodology, and identifying analytical errors. It covers claim verification protocols, red flags in reporting, common statistical pitfalls, significance testing guidance, and causation assessment criteria to help you audit data analysis and fact-check research findings.
Use it when
- Statistics Verifier identifies analytical errors, biases, and methodological flaws through systematic review of study design.
- Yes.
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Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How does Statistics Verifier help fact check statistical claims?
Statistics Verifier provides structured checklists and frameworks for validating statistical claims by guiding you through claim verification protocols, red flags in reporting, and common statistical pitfalls. It helps you systematically audit data analysis and fact-check research findings against established standards.
What methods does Statistics Verifier use to detect bias in data analysis?
Statistics Verifier identifies analytical errors, biases, and methodological flaws through systematic review of study design, sample selection, and analysis procedures. It flags issues like selection bias, confounding variables, p-hacking, and HARKing that can distort results and compromise research validity.
Can Statistics Verifier help verify research findings methodology?
Yes. Statistics Verifier assesses study quality by evaluating sample size adequacy, statistical significance testing, effect sizes, and confidence intervals. It provides guidance on significance testing, power analysis, and reproducibility checklists to validate whether research methodology meets scientific standards.
How does Statistics Verifier distinguish correlation from causation?
Statistics Verifier applies causation assessment criteria, including Bradford Hill causation principles, to evaluate whether claimed causal relationships are justified. It helps you identify confounding variables and assess whether study design supports causal inference or merely shows correlation.
What red flags does Statistics Verifier identify in statistical reporting?
Statistics Verifier highlights misleading statistics red flags including improper p-value interpretation, multiple comparisons without correction, inadequate sample sizes, and data visualization integrity issues. It also flags reproducibility concerns and checks for common reporting errors that undermine research credibility.
How can Statistics Verifier improve data visualization integrity assessment?
Statistics Verifier evaluates data visualization integrity and reporting standards to ensure charts, graphs, and tables accurately represent underlying data. It checks for misleading scales, omitted context, and visual distortions that could misrepresent statistical findings or manipulate interpretation.
SKILL.md
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Statistics Verifier
Structured frameworks for verifying statistical claims, validating research methodology, and detecting analytical errors and biases.
Statistical Claim Verification Checklist
Rapid Claim Assessment
``` CLAIM VERIFICATION PROTOCOL:
- SOURCE CHECK
- Who made the claim?
- What is their expertise and incentive?
- Where was it published (peer-reviewed, preprint, press release)?
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Is the original data or study accessible?
-
METHODOLOGY CHECK
- What type of study (RCT, observational, survey, meta-analysis)?
- What was the sample size and population?
- What was the measurement method?
-
Is the statistical test appropriate for the data type?
-
NUMBER SENSE CHECK
- Does the claim pass a basic plausibility test?
- Are units and denominators clearly stated?
- Absolute vs relative numbers — which is being used?
- Is the base rate provided for context?
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