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name: data-literacy-for-non-specialists description: "Read charts, common statistics, and study designs without overclaiming — for essays, labs, and civic numeracy."

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.