Classical Statistical Inference

Željko Ivezi, Andrew J. Connolly, Jacob T. VanderPlas, Alexander Gray, Željko Ivezi, Andrew J. Connolly, Jacob T. VanderPlas and Alexander Gray

in Statistics, Data Mining, and Machine Learning in Astronomy

Published by Princeton University Press

Published in print January 2014 | ISBN: 9780691151687
Published online October 2017 | e-ISBN: 9781400848911
Classical Statistical Inference

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This chapter introduces the main concepts of statistical inference, or drawing conclusions from data. There are three main types of inference: point estimation, confidence estimation, and hypothesis testing. There are two major statistical paradigms which address the statistical inference questions: the classical, or frequentist paradigm, and the Bayesian paradigm. While most of statistics and machine learning is based on the classical paradigm, Bayesian techniques are being embraced by the statistical and scientific communities at an ever-increasing pace. The chapter begins with a short comparison of classical and Bayesian paradigms, and then discusses the three main types of statistical inference from the classical point of view.

Keywords: statistical inference; point estimation; confidence estimation; hypothesis testing; Bayesian paradigm; frequentist paradigm

Chapter.  21734 words.  Illustrated.

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