The Three Revolutions in Parametric Statistical Inference. - The Three Revolutions in Parametric Statistical Inference. - Binomial Statistical Inference. - James Bernoulli's Law of Large Numbers for the Binomial 1713 and Its Generalization. - De Moivre's Normal Approximation to the Binomial 1733 and Its Generalization. - Bayes's Posterior Distribution of the Binomial Parameter and His Rule for Inductive Inference 1764. - Statistical Inference by Inverse Probability. - Laplace's Theory of Inverse Probability 17741786. - A Nonprobabilistic Interlude: The Fitting of Equations to Data 17501805. - Gauss's Derivation of the Normal Distribution and the Method of Least Squares 1809. - Credibility and Confidence Intervals by Laplace and Gauss. - The Multivariate Posterior Distribution. - Edgeworth's Genuine Inverse Method and the Equivalence of Inverse and Direct Probability in Large Samples 1908 and 1909. - Criticisms of Inverse Probability. - The Central Limit Theorem and Linear Minimum Variance Estimation by Laplace and Gauss. - Laplace's Central Limit Theorem and Linear Minimum Variance Estimation. - Gauss's Theory of Linear Minimum Variance Estimation. - Error Theory. Skew Distributions. Correlation. Sampling Distributions. - The Development of a Frequentist Error Theory. - Skew Distributions and the Method of Moments. - Normal Correlation and Regression. - Sampling Distributions Under Normality 18761908. - The Fisherian Revolution 19121935. - Fisher's Early Papers 19121921. - The Revolutionary Paper 1922. - Studentization the F Distribution and the Analysis of Variance 19221925. - The Likelihood Function Ancillarity and Conditional Inference. Language: English