Statistical hypothesis testing

About: Statistical hypothesis testing is a research topic. Over the lifetime, 19580 publications have been published within this topic receiving 1037815 citations. The topic is also known as: statistical hypothesis testing & confirmatory data analysis.

Confirmation, Disconfirmation, and Information in Hypothesis Testing

Abstract: Strategies for hypothesis testing in scientific investigation and everyday reasoning have interested both psychologists and philosophers. A number of these scholars stress the importance of disconfir. marion in reasoning and suggest that people are instead prone to a general deleterious "confirmation bias" In particula~ it is suggested that people tend to test those cases that have the best chance of verifying current beliefs rather than those that have the best chance of falsifying them. We show, howeve~ that many phenomena labeled "confirmation bias" are better understood in terms of a general positive test strate~. With this strategy, there is a tendency to test cases that are expected (or known) to have the property of interest rather than those expected (or known) to lack that property. This strategy is not equivalent to confirmation bias in the first sense; we show that the positive test strategy can be a very good heuristic for determining the truth or falsity of a hypothesis under realistic conditions~ It can, howeve~ lead to systematic errors or inefficiencies. The appropriateness of human hypotheses-testing strategies and prescriptions about optimal strategies must he understood in terms of the interaction between the strategy and the task at hand.

A re-evaluation of random-effects meta-analysis

Abstract: Meta-analysis in the presence of unexplained heterogeneity is frequently undertaken by using a random-effects model, in which the effects underlying different studies are assumed to be drawn from a normal distribution. Here we discuss the justification and interpretation of such models, by addressing in turn the aims of estimation, prediction and hypothesis testing. A particular issue that we consider is the distinction between inference on the mean of the random-effects distribution and inference on the whole distribution. We suggest that random-effects meta-analyses as currently conducted often fail to provide the key results, and we investigate the extent to which distribution-free, classical and Bayesian approaches can provide satisfactory methods. We conclude that the Bayesian approach has the advantage of naturally allowing for full uncertainty, especially for prediction. However, it is not without problems, including computational intensity and sensitivity to a priori judgements. We propose a simple prediction interval for classical meta-analysis and offer extensions to standard practice of Bayesian meta-analysis, making use of an example of studies of 'set shifting' ability in people with eating disorders.

Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses

Abstract: This book provides a step-by-step manual on the application of permutation tests in biology, medicine, science, and engineering. Its intuitive and informal style will ideally suit it as a text for students and researchers coming to these methods for the first time. In particular, it shows how the problems of missing and censored data, nonresponders, after-the-fact covariates, and outliers may be handled.

Measurement Error in Nonlinear Models

Abstract: Preface Guide to Notation 1. Introduction 2. Regression and Attenuation 3. Regression Calibration 4. Simulation Extrapolation 5. Instrumental Variables 6. Functional Methods 7. Likelihood and Quasilikelihood 8. Bayesian Methods 9. Semiparametric Methods 10. Unknown Link Functions 11. Hypothesis Testing 12. Density Estimation and Nonparametric Regression 13. Response Variable Error 14. Other Topics Appendix: Fitting Methods and Models References Author Index Subject Index