Most exam stats are not complicated — they’re just unfamiliar under time pressure. The goal is a tiny set of automatic moves: when to use likelihood ratios, how to convert ARR to NNT, and how to interpret odds ratios without getting tricked by intuition.
The minimum set to automate
LR+ and LR- (diagnostics), ARR → NNT (treatment effect), and odds ratio interpretation (association). If you can do these fast, you unlock a lot of marks.
1
Move 1 — Use likelihood ratios to update probability
Know what they’re for: translating a test result into post-test probability. LR is often more stable across populations than predictive values, which shift with prevalence.
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Move 2 — NNT is just 1 / ARR
If ARR is 0.10 (10%), NNT is 10. If ARR is 0.02 (2%), NNT is 50. Practise 3–5 quick examples until it’s automatic.
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Move 3 — Odds ratios aren’t risk ratios
ORs can overstate effect size when outcomes are common. Don’t over-interpret magnitude; focus on direction, confidence interval, and context.
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Move 4 — Use a 60-second stats drill
Once daily: one LR question, one NNT question, one OR interpretation. Small reps beat long painful cramming.
Common failure mode
Memorising definitions but never doing timed examples. Stats is a skill: you need retrieval under pressure.
SourceOxford CEBM — Likelihood ratios (tool + explanation)
Open Link SourceJAMAevidence — Numbers Needed to Treat (NNT = 1/ARR)
Open Link SourceJAMA Guide — Correct use and interpretation of odds ratios
Open Link