The beginning: the question -- Ambiguity: statistics -- Fodder: data -- Data: measurement -- Data structure: levels of measurement -- Simplifying: groups and clusters -- Counts: frequencies -- Pictures: graphs -- Scatterings: distributions -- Bell-shaped: the normal curve -- Lopsidedness: skewness -- Averages: central tendencies -- Two types: descriptive and inferential -- Foundations: assumptions -- Murkiness: missing data -- Leeway: robustness -- Consistency: reliability -- Truth: validity -- Unpredictability: randomness -- Representatives: samples -- Mistakes: error -- Real or not: outliers -- Impediments: confounds -- Nuisances: covariates -- Background: independent variables -- Targets: dependent variables -- Inequality: standard deviations and variance -- Prove: no, falsify -- No difference: the null hypothesis -- Reductionism: models -- Risk: probability -- Uncertainty: p values -- Expectations: chi-square -- Importance vs. difference: substantive vs. statistical significance -- Strength: power -- Impact: effect sizes -- Likely range: confidence intervals -- Association: correlation -- Predictions: multiple regression -- Abundance: multivariate analysis -- Differences: t tests and analysis of variance -- Differences that matter: discriminant analysis -- Both sides loaded: canonical covariance analysis -- Nesting: hierarchical models -- Cohesion: factor analysis -- Ordered events: path analysis -- Digging deeper: structural equation models -- Abundance: big data -- Scarcity: small data -- Fiddling: modifications and new techniques -- Epilogue.