Getting started
Spotting questionable numbers
Background
Statistical benchmarks
Severity and frequency
Varieties of dubious data
Blunders
The slippery decimal point
Botched translations
Misleading graphs
Careless calculations
Sources: who counted
and why?
Big round numbers
Hyperbole
Shocking claims
Naming the problem
Definitions: what did they count?
Broad definitions
Expanding definitions
Changing definitions
The uncounted
Measurements: how did they count?
Creating measures
Odd units of analysis
Loaded questions
Raising the bar
Technical measures
Packaging: what are they telling us?
Impressive formats
Misleading samples
Convenient time frames
Peculiar percentages
Selective comparisons
Statistical milestones
Averages
Epidemics
Correlations
Discoveries
Debates: what if they disagree?
Causality debates
Equality debates
Policy debates
Stat-spotting on your own
Summary: common signs of dubious data
Better data: some characteristics
Afterword: if you had no idea things were that bad, they probably aren't
Suggestions for those who want to continue stat-spotting.