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Foundations of statistics is the usual name for the epistemological debate in statistics over how one should conduct inductive inference from data. Among the issues considered in statistical inference are the question of Bayesian inference versus frequentist inference, the distinction between Fisher's "significance testing" and Neyman-Pearson "hypothesis testing", and whether the likelihood principle should be followed. Some of these issues have been debated for up to 200 years without resolution. Bandyopadhyay & Forster describe four statistical paradigms: "(1) classical statistics or error statistics, (ii) Bayesian statistics, (iii) likelihood-based statistics, and (iv) the Akaikean-Information Criterion-based statistics". Savage's text ''Foundations of Statistics'' has been cited over 12000 times on Google Scholar.〔(Citations of Savage (1972) )〕 It tells the following. ==Fisher's "significance testing" vs Neyman-Pearson "hypothesis testing"== In the development of classical statistics in the second quarter of the 20th century two competing models of inductive statistical testing were developed. Their relative merits were hotly debated (for over 25 years) until Fisher's death. While a hybrid of the two methods is widely taught and used, the philosophical questions raised in the debate have not been resolved. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「foundations of statistics」の詳細全文を読む スポンサード リンク
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