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.

Essentials of Econometrics

Abstract: Chapter 1: The Nature and Scope of Econometrics Part I: The Linear Regression Model Chapter 2: Basic Ideas of Linear Regression Chapter 3: The Two-Variable Model: Hypothesis Testing Chapter 4: Multiple Regression: Estimation and Hypothesis Testing Chapter 5: Functional Forms of Regression Models Chapter 6: Dummy Variable Regression Models Part II: Regression Analysis in Practice Chapter 7: Model Selection: Criteria and Tests Chapter 8: Multicollinearity: What Happens if Explanatory Variables are Correlated? Chapter 9: Heteroscedasticity: What Happens if the Error Variance is Nonconstant? Chapter 10: What Happens if Error Terms are Correlated? Part II: Advanced Topics in Econometrics Chapter 11: Simultaneous Equation Models Chapter 12: Selected Topics in Single-Equation Regression Models Appendices Introduction: Basics of Probability and Statistics Appendix A: Review of Statistics: Probability and Probability Distributions Appendix B: Characteristics of Probability Distributions Appendix C: Some Important Probability Distributions Appendix D: Statistical Inference: Estimation and Hypothesis Testing Appendix E: Statistical Tables Appendix F: Computer Output of EViews, Minitab, Excel, and STATA

The Insignificance of Statistical Significance Testing

Abstract: Despite their wide use in scientific journals such as The Journal of Wildlife Management, statistical hypothesis tests add very little value to the products of research. Indeed, they frequently confuse the interpretation of data. This paper describes how statistical hypothesis tests are often viewed, and then contrasts that interpretation with the correct one. I discuss the arbitrariness of P-values, conclusions that the null hypothesis is true, power analysis, and distinctions between statistical and biological significance. Statistical hypothesis testing, in which the null hypothesis about the properties of a population is almost always known a priori to be false, is contrasted with scientific hypothesis testing, which examines a credible null hypothesis about phenomena in nature. More meaningful alternatives are briefly outlined, including estimation and confidence intervals for determining the importance of factors, decision theory for guiding actions in the face of uncertainty, and Bayesian approaches to hypothesis testing and other statistical practices.

Small-sample estimation of negative binomial dispersion, with applications to SAGE data

Abstract: We derive a quantile-adjusted conditional maximum likelihood estimator for the dispersion parameter of the negative binomial distribution and compare its performance, in terms of bias, to various other methods. Our estimation scheme outperforms all other methods in very small samples, typical of those from serial analysis of gene expression studies, the motivating data for this study. The impact of dispersion estimation on hypothesis testing is studied. We derive an "exact" test that outperforms the standard approximate asymptotic tests.

Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain

Abstract: The authors describe almost entirely automated procedures for estimation of global, voxel, and cluster-level statistics to test the null hypothesis of zero neuroanatomical difference between two groups of structural magnetic resonance imaging (MRI) data. Theoretical distributions under the null hypothesis are available for (1) global tissue class volumes; (2) standardized linear model [analysis of variance (ANOVA and ANCOVA)] coefficients estimated at each voxel; and (3) an area of spatially connected clusters generated by applying an arbitrary threshold to a two-dimensional (2-D) map of normal statistics at voxel level. The authors describe novel methods for economically ascertaining probability distributions under the null hypothesis, with fewer assumptions, by permutation of the observed data. Nominal Type I error control by permutation testing is generally excellent; whereas theoretical distributions may be over conservative. Permutation has the additional advantage that it can be used to test any statistic of interest, such as the sum of suprathreshold voxel statistics in a cluster (or cluster mass), regardless of its theoretical tractability under the null hypothesis. These issues are illustrated by application to MRI data acquired from 18 adolescents with hyperkinetic disorder and 16 control subjects matched for age and gender.