Statistical Inference: A Commentary for the Social and Behavioural Sciences

by Michael Oakes

Critical analysis of the use of statistical inference in social and behavioural research, its proper role, logic, and its abuse. Argues that ignorance and misunderstanding of the role of statistical inference has had a detrimental effect upon research in the field. Points the way to an appropriate appreciation of the part played by quantification in these disciplines. Examines the significance test versus interval estimation and evaluates Neyman-Pearson,...

• Format: Hardcover
• Language: English
• ISBN: 0471104434
• ISBN13: 9780471104438
• Release Date: May 1986
• Publisher: Wiley
• Length: 196 Pages
• Weight: 1.10 lbs.

Customer Reviews

Must read - shame you can't!

What a shame that this wonderful book is now out of print. Frankly it ought to be compulsory reading for anyone who ever ventures near a statistical test. Anyone who understands the assumptions behind a t-test should be able to grasp (and enjoy?) the arguments.

Completely worth the effort

I found this short, 182-page book to be dense, demanding, extraordinarily insightful, and extremely rewarding. It prodded me to learn more about many subjects related to the scientific method, inference in research, and statistical methods. I consider it one of the 15 or 20 most important books I have ever read, and one of the 5 most important about quantitative research (which is how I make my living). Certainly I think of it as essential for any teacher of statistics. Prerequisites for reading this book would be about 3 courses in statistics, deep curiosity about statistics, and strong motivation to understand the concepts behind statistical inference. Oakes uses very little math; instead he uses rigorous, clever, incisive logic to delve into what statistical findings such as p-values really mean, how we should interpret statistical results, and what value significance tests might hold relative to statements about confidence intervals or effect sizes. In the process, he touches insightfully on a number of developments in the history of the field. (His sense of humor isn't bad, either.) Oakes does an excellent job when, in the tradition of Amos Tversky, Daniel Kahneman, and Gerd Gigerenzer (as well as, in a more technical statistical way, Elazar Pedhazur), he exposes misconceptions about statistical inference that plague the work of so many, including, it seems, the vast majority of social scientists. You may find yourself doing double takes at his treatment of some of these misconceptions. Oakes also gives a short, fascinating treatment of the polemics surrounding competing schools of statistical inference, including the methods termed Fisherian, Neyman-Pearson, Bayesian, and Likelihood. A dynamite finish. This book has no weak spots.