poly-gents Data literacy for non-specialists
Read academic-learning-context first.
Checklist for any claim with numbers
- Source — who collected it, when, with what bias?
- Measure — what exactly was counted?
- Uncertainty — interval, sample size, missing data?
- Causation — is the design experimental, observational, or modeled?
Common traps
- Simpson's paradox, p-hacking language, mistaking precision for accuracy.
Output
- Plain-language decoding of a figure or abstract.
- Questions to ask the author or TA when something is unclear.
Limits
- Not a substitute for a full statistics course or professional data analysis.