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.

Modelling and quantitative methods in fisheries

Abstract: Fisheries and Modelling Fish Population Dynamics The Objectives of Stock Assessment Characteristics of Mathematical Models Types of Model Structure Simple Population Models Introduction Assumptions-Explicit and Implicit Density-Independent Growth Density-Dependent Models Responses to Fishing Pressure The Logistic Model in Fisheries Age-Structured Models Simple Yield-per-Recruit Model Parameter Estimation Models and Data Least Squared Residuals Nonlinear Estimation Likelihood Bayes' Theorem Concluding Remarks Computer-Intensive Methods Introduction Resampling Randomization Tests Jackknife Methods Bootstrapping Methods Monte Carlo Methods Bayesian Methods Relationships between Methods Computer Programming Randomization Tests Introduction Hypothesis Testing Randomization of Structured Data Statistical Bootstrap Methods The Jackknife and Pseudo Values The Bootstrap Bootstrap Statistics Bootstrap Confidence Intervals Concluding Remarks Monte Carlo Modelling Monte Carlo Models Practical Requirements A Simple Population Model A Non-Equilibrium Catch Curve Concluding Remarks Characterization of Uncertainty Introduction Asymptotic Standard Errors Percentile Confidence Intervals Using Likelihoods Likelihood Profile Confidence Intervals Percentile Likelihood Profiles for Model Outputs Markov Chain Monte Carlo (MCMC) Conclusion Growth of Individuals Growth in Size von Bertalanffy Growth Model Alternatives to von Bertalanffy Comparing Growth Curves Concluding Remarks Stock Recruitment Relationships Recruitment and Fisheries Stock Recruitment Biology Beverton-Holt Recruitment Model Ricker Model Deriso's Generalized Model Residual Error Structure The Impact of Measurement Errors Environmental Influences Recruitment in Age-Structured Models Concluding Remarks Surplus Production Models Introduction Equilibrium Methods Surplus Production Models Observation Error Estimates Beyond Simple Models Uncertainty of Parameter Estimates Risk Assessment Projections Practical Considerations Conclusions Age-Structured Models Types of Models Cohort Analysis Statistical Catch-at-Age Concluding Remarks Size-Based Models Introduction The Model Structure Conclusion Appendix: The Use of Excel in Fisheries Bibliography Index

Bayesian inference for psychology. Part II: Example applications with JASP

Abstract: Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP (http://www.jasp-stats.org), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder’s BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.

Optimal Inference in Cointegrated Systems

Abstract: Properties of maximum likelihood estimates of cointegrated systems are studied. Alternative formulations are considered, including a new triangular system error correction mechanism. We demonstrate that full system maximum likelihood brings the problem of inference within the family covered by the locally asymptotically mixed normal asymptotic theory, provided all unit roots have been eliminated by specification and data transformation. Methodological issues provide a major focus of the paper. Our results favor use of full system estimation in error correction mechanisms or subsystem methods that are asymptotically equivalent. They also point to disadvantages in the use of unrestricted VAR's formulated in levels and of certain single equation approaches to estimation of error correction mechanisms. Copyright 1991 by The Econometric Society.

Business Research Methods and Statistics Using SPSS

Abstract: PART ONE: GENERAL ORIENTATION TO RESEARCH IN BUSINESS AND MANAGEMENT Research, Statistics and Business Decisions Contrasting Philosophies and Approaches to Research Ethical Issues in Research Selecting the Topic and Conducting a Literature Review Theory, Problem Definition, Frameworks and Research Design PART TWO: ENTERING, DESCRIBING AND OBTAINING DATA How To Enter Data into SPSS and Undertake Initial Screening Describing and Presenting your Data Normal Distribution, Probability and Statistical Significance Sampling Issues Hypothesis Formation, Types of Error and Estimation Power and Effect Size PART THREE: STATISTICALLY ANALYSING DATA Hypothesis Testing for Differences between Means and Proportions Analysis of Variance Techniques (ANOVA) Chi Square Methods of Correlation Testing Hypotheses of Relationships Prediction and Regression Reliability and Validity Factor Analysis PART FOUR: SURVEY METHODS FOR RESEARCH IN BUSINESS AND MANAGEMENT Attitude Questionnaires and Measurement Structured Interview and Questionnaires Surveys PART FIVE: REPORTING AND PRESENTING RESEARCH Writing Up and Communicating Research

Stochastic Complexity and Modeling

Abstract: As a modification of the notion of algorithmic complexity, the stochastic complexity of a string of data, relative to a class of probabilistic models, is defined to be the fewest number of binary digits with which the data can be encoded by taking advantage of the selected models. The computation of the stochastic complexity produces a model, which may be taken to incorporate all the statistical information in the data that can be extracted with the chosen model class. This model, for example, allows for optimal prediction, and its parameters are optimized both in their values and their number. A fundamental theorem is proved which gives a lower bound for the code length and, therefore, for prediction errors as well. Finally, the notions of "prior information" and the "useful information" in the data are defined in a new way, and a related construct gives a universal test statistic for hypothesis testing.