A History of Parametric Statistical Inference from Bernoulli to Fisher, 1713–1935

Chapter 1 The Three Revolutions in Parametric Statistical Inference

Chapter 2 James Bernoulli’s Law of Large Numbers for the Binomial, 1713, and Its Generalization

Chapter 3 De Moivre’s Normal Approximation to the Binomial, 1733, and Its Generalization

Chapter 4 Bayes’s Posterior Distribution of the Binomial Parameter and His Rule for Inductive Inference, 1764

Chapter 5 Laplace’s Theory of Inverse Probability, 1774–1786

Chapter 6 A Nonprobabilistic Interlude: The Fitting of Equations to Data, 1750–1805

Chapter 7 Gauss’s Derivation of the Normal Distribution and the Method of Least Squares, 1809

Chapter 8 Credibility and Confidence Intervals by Laplace and Gauss

Chapter 9 The Multivariate Posterior Distribution

Chapter 10 Edgeworth’s Genuine Inverse Method and the Equivalence of Inverse and Direct Probability in Large Samples, 1908 and 1909

Chapter 11 Criticisms of Inverse Probability

Chapter 12 Laplace’s Central Limit Theorem and Linear Minimum Variance Estimation

Chapter 13 Gauss’s Theory of Linear Minimum Variance Estimation

Chapter 14 The Development of a Frequentist Error Theory

Chapter 15 Skew Distributions and the Method of Moments

Chapter 16 Normal Correlation and Regression

Chapter 17 Sampling Distributions Under Normality, 1876–1908

Chapter 18 Fisher’s Early Papers, 1912–1921

Chapter 19 The Revolutionary Paper, 1922

Chapter 20 Studentization, the F Distribution, and the Analysis of Variance, 1922–1925

Chapter 21 The Likelihood Function, Ancillarity, and Conditional Inference