Statistical literacy is the ability to read claims critically, question data quality, and avoid misleading conclusions.

Questions to ask about a claim

• Who collected the data and why?
• How large and representative is the sample?
• How was the data measured?
• Are conclusions consistent with evidence?

Sampling and bias

Biased samples produce unreliable conclusions. Prefer random, sufficiently large, and representative samples.

Correlation vs causation

Correlation means variables move together. It does not prove one causes the other. Confounders may explain both.

Misleading charts

• Truncated axes exaggerating differences.
• Inconsistent scales.
• Cherry-picked time ranges.
• 3D chart distortion.

Interpreting risk language

Distinguish absolute change from relative change. “Risk doubled” may still represent a very small absolute increase.

Exam strategy

• Quote numerical evidence when critiquing claims.
• Mention possible bias/confounding clearly.
• Use “suggests” unless causation is justified by design.
• Comment on uncertainty and data limitations.