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.

Introduction to probability and statistics for engineers and scientists

Abstract: Preface Introduction to Statistics Descriptive Statistics Elements of Probability Random Variables and Expectation Special Random Variables Distributions of Sampling Statistics Parameter Estimation Hypothesis Testing Regression Analysis of Variance Goodness of Fit Tests and Categorical Data Analysis Nonparametric Hypothesis Tests Quality Control LifeTesting Appendix of Tables

The Rules of the Game Called Psychological Science

Abstract: If science were a game, a dominant rule would probably be to collect results that are statistically significant. Several reviews of the psychological literature have shown that around 96% of papers involving the use of null hypothesis significance testing report significant outcomes for their main results but that the typical studies are insufficiently powerful for such a track record. We explain this paradox by showing that the use of several small underpowered samples often represents a more efficient research strategy (in terms of finding p < .05) than does the use of one larger (more powerful) sample. Publication bias and the most efficient strategy lead to inflated effects and high rates of false positives, especially when researchers also resorted to questionable research practices, such as adding participants after intermediate testing. We provide simulations that highlight the severity of such biases in meta-analyses. We consider 13 meta-analyses covering 281 primary studies in various fields of psychology and find indications of biases and/or an excess of significant results in seven. These results highlight the need for sufficiently powerful replications and changes in journal policies.

A kernel statistical test of independence

Abstract: Although kernel measures of independence have been widely applied in machine learning (notably in kernel ICA), there is as yet no method to determine whether they have detected statistically significant dependence. We provide a novel test of the independence hypothesis for one particular kernel independence measure, the Hilbert-Schmidt independence criterion (HSIC). The resulting test costs O(m2), wherem is the sample size. We demonstrate that this test outperforms established contingency table and functional correlation-based tests, and that this advantage is greater for multivariate data. Finally, we show the HSIC test also applies to text (and to structured data more generally), for which no other independence test presently exists.

Statistical inference based on divergence measures

Abstract: DIVERGENCE MEASURES: DEFINITION AND PROPERTIES Introduction Phi-divergence. Measures between Two Probability Distributions: Definition and Properties Other Divergence Measures between Two Probability Distributions Divergence among k Populations Phi-disparities Exercises Answers to Exercises ENTROPY AS A MEASURE OF DIVERSITY: SAMPLING DISTRIBUTIONS Introduction Phi-entropies. Asymptotic Distribution Testing and Confidence Intervals for Phi-entropies Multinomial Populations: Asymptotic Distributions Maximum Entropy Principle and Statistical Inference on Condensed Ordered Data Exercises Answers to Exercises GOODNESS-OF-FIT: SIMPLE NULL HYPOTHESIS Introduction Phi-divergences and Goodness-of-fit with Fixed Number of Classes Phi-divergence Test Statistics under Sparseness Assumptions Nonstandard Problems: Tests Statistics based on Phi-divergences Exercises Answers to Exercises OPTIMALITY OF PHI-DIVERGENCE TEST STATISTICS IN GOODNESS-OF-FIT Introduction Asymptotic Effciency Exact and Asymptotic Moments: Comparison A Second Order Approximation to the Exact Distribution Exact Powers Based on Exact Critical Regions Small Sample Comparisons for the Phi-divergence Test Statistics Exercises Answers to Exercises MINIMUM PHI-DIVERGENCE ESTIMATORS Introduction Maximum Likelihood and Minimum Phi-divergence Estimators Properties of the Minimum Phi-divergence Estimator Normal Mixtures: Minimum Phi-divergence Estimator Minimum Phi-divergence Estimator with Constraints: Properties Exercises Answers to Exercises GOODNESS-OF-FIT: COMPOSITE NULL HYPOTHESIS Introduction Asymptotic Distribution with Fixed Number of Classes Nonstandard Problems: Test Statistics Based on Phi-divergences Exercises Answers to Exercises Testing Loglinear Models Using Phi-divergence Test Statistics Introduction Loglinear Models: Definition Asymptotic Results for Minimum Phi-divergence Estimators in Loglinear Models Testing in Loglinear Models Simulation Study Exercises Answers to Exercises PHI-DIVERGENCE MEASURES IN CONTINGENCY TABLES Introduction Independence Symmetry Marginal Homogeneity Quasi-symmetry Homogeneity Exercises Answers to Exercises TESTING IN GENERAL POPULATIONS Introduction Simple Null Hypotheses: Wald, Rao, Wilks and Phi-divergence Test Statistics Composite Null Hypothesis Multi-sample Problem Some Topics in Multivariate Analysis Exercises Answers to Exercises References Index