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.

Toward Evidence-Based Medical Statistics. 1: The P Value Fallacy

Abstract: An important problem exists in the interpretation of modern medical research data: Biological understanding and previous research play little formal role in the interpretation of quantitative results. This phenomenon is manifest in the discussion sections of research articles and ultimately can affect the reliability of conclusions. The standard statistical approach has created this situation by promoting the illusion that conclusions can be produced with certain "error rates," without consideration of information from outside the experiment. This statistical approach, the key components of which are P values and hypothesis tests, is widely perceived as a mathematically coherent approach to inference. There is little appreciation in the medical community that the methodology is an amalgam of incompatible elements, whose utility for scientific inference has been the subject of intense debate among statisticians for almost 70 years. This article introduces some of the key elements of that debate and traces the appeal and adverse impact of this methodology to the P value fallacy, the mistaken idea that a single number can capture both the long-run outcomes of an experiment and the evidential meaning of a single result. This argument is made as a prelude to the suggestion that another measure of evidence should be used--the Bayes factor, which properly separates issues of long-run behavior from evidential strength and allows the integration of background knowledge with statistical findings.

Statistical data analysis

Abstract: Preface Notation 1. Fundamental Concepts 2. Examples of Probability Functions 3. The Monte Carlo Method 4. Statistical Tests 5. General Concepts of Parameter Estimation 6. The Method of Maximum Likelihood 7. The Method of Least Squares 8. The Method of Moments 9. Statistical Errors, Confidence Intervals and Limits 10. Characteristic Functions and Related Examples 11. Unfolding Bibliography Index

Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings

Abstract: Software defect prediction strives to improve software quality and testing efficiency by constructing predictive classification models from code attributes to enable a timely identification of fault-prone modules. Several classification models have been evaluated for this task. However, due to inconsistent findings regarding the superiority of one classifier over another and the usefulness of metric-based classification in general, more research is needed to improve convergence across studies and further advance confidence in experimental results. We consider three potential sources for bias: comparing classifiers over one or a small number of proprietary data sets, relying on accuracy indicators that are conceptually inappropriate for software defect prediction and cross-study comparisons, and, finally, limited use of statistical testing procedures to secure empirical findings. To remedy these problems, a framework for comparative software defect prediction experiments is proposed and applied in a large-scale empirical comparison of 22 classifiers over 10 public domain data sets from the NASA Metrics Data repository. Overall, an appealing degree of predictive accuracy is observed, which supports the view that metric-based classification is useful. However, our results indicate that the importance of the particular classification algorithm may be less than previously assumed since no significant performance differences could be detected among the top 17 classifiers.

Applied Multivariate Statistical Analysis

Abstract: I Descriptive Techniques: Comparison of Batches.- II Multivariate Random Variables: A Short Excursion into Matrix Algebra Moving to Higher Dimensions Multivariate Distributions Theory of the Multinormal Theory of Estimation Hypothesis Testing.- III Multivariate Techniques: Decomposition of Data Matrices by Factors Principal Components Analysis Factor Analysis Cluster Analysis Discriminant Analysis.- Correspondence Analysis.- Canonical Correlation Analysis.- Multidimensional Scaling.- Conjoint Measurement Analysis.- Application in Finance.- Computationally Intensive Techniques.- A: Symbols and Notations.- B: Data.- Bibliography.- Index.

Quantitative Data Analysis with SPSS for Windows: A Guide for Social Scientists

Abstract: From the Publisher: Quantitative Data Analysis with SPSS for Windows explains statistical tests using the latest version of SPSS, the most widely used computer package for analyzing quantitative data. Using the same formula-free, non-technical approach as the highly successful non-windows version, it assumes no previous familiarity with either statistics or computing, and takes the reader step-by-step through each of the techniques for which SPSS for Windows can be used. The book also contains exercises with answers, and covers issues such as sampling, statistical significance, and the selection of appropriate tests.