Errors in Statistical Inference Under Model Misspecification: Evidence, Hypothesis Testing, and AIC

Methodology in Practice: Statistical Misspecification Testing

The growing availability of computer power and statistical software has greatly increased the ease with which practitioners apply statistical methods, but this has not been accompanied by attention to checking the assumptions on which these methods are based. At the same time, disagreements about inferences based on statistical research frequently revolve around whether the assumptions are actu...

A comparative introduction to statistical inference and hypothesis testing

These are some notes on a very simple comparative introduction to four basic approaches of statistical inference—Fisher, Neyman–Pearson, Fisher/Neyman–Pearson hybrid, and Bayes—from a course on Quantitative & Statistical Reasoning at OU in Fall 2016. In particular, I hope to give a rough understanding of the differences between the frequentist and Bayesian paradigms, though they are not entirel...

BASIC STATISTICS FOR BUSY CLINICIANS ( V ) Statistical inference : Hypothesis testing

The aim of statistical inference is to predict the parameters of a population, based on a sample of data. Inferential statistics encompasses the estimation of parameters and model predictions. The present article describes the hypothesis tests or statistical significance tests most commonly used in healthcare research. & 2010 SEICAP. Published by Elsevier España, S.L. All rights reserved.

Phylogenetic Inference and Hypothesis Testing

Statistical Hypothesis Testing Basics

1 From the Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (K.H.Z., J.R.F., S.G.S., C.M.C.T.); and Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave, Boston, MA 02115 (K.H.Z.). Received September 10, 2001; revision requested November 8; revision received December 12; accepted December 19. Supported in part by Public Health Ser...