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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.


Papers
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Journal ArticleDOI
TL;DR: The Bayesian advantages of the newly developed statistical software program JASP are discussed using real data on the relation between Quality of Life and Executive Functioning in children with Autism Spectrum Disorder.
Abstract: We illustrate the Bayesian approach to data analysis using the newly developed statistical software program JASP. With JASP, researchers are able to take advantage of the benefits that the Bayesian framework has to offer in terms of parameter estimation and hypothesis testing. The Bayesian advantages are discussed using real data on the relation between Quality of Life and Executive Functioning in children with Autism Spectrum Disorder.

182 citations

Journal ArticleDOI
TL;DR: The relationship between p-values and minimum Bayes factors also depends on the sample size and on the dimension of the parameter of interest as discussed by the authors, and the relationship between the two-sided significance tests for a point null hypothesis in more detail.
Abstract: The p-value quantifies the discrepancy between the data and a null hypothesis of interest, usually the assumption of no difference or no effect. A Bayesian approach allows the calibration of p-values by transforming them to direct measures of the evidence against the null hypothesis, so-called Bayes factors. We review the available literature in this area and consider two-sided significance tests for a point null hypothesis in more detail. We distinguish simple from local alternative hypotheses and contrast traditional Bayes factors based on the data with Bayes factors based on p-values or test statistics. A well-known finding is that the minimum Bayes factor, the smallest possible Bayes factor within a certain class of alternative hypotheses, provides less evidence against the null hypothesis than the corresponding p-value might suggest. It is less known that the relationship between p-values and minimum Bayes factors also depends on the sample size and on the dimension of the parameter of interest. We i...

182 citations

Book
01 Jan 2005
TL;DR: In this article, the authors present a model for Bayesian inference based on decision theory and higher-order theory with special models and two-sided tests and conditional inference, using bootstrap methods.
Abstract: 1. Introduction 2. Decision theory 3. Bayesian methods 4. Hypothesis testing 5. Special models 6. Sufficiency and completeness 7. Two-sided tests and conditional inference 8. Likelihood theory 9. Higher-order theory 10. Predictive inference 11. Bootstrap methods.

182 citations

Journal ArticleDOI
Roger E. Kirk1
TL;DR: A null hypothesis significance test does not tell us how large the effect is or whether it is important or useful as discussed by the authors, but rather it tells us the probability of obtaining the effect or a more extreme effect if the null hypothesis is true.
Abstract: Researchers want to answer three basic questions: (a) Is an observed effect real or should it be attributed to chance? (b) If the effect is real, how large is it? and (c) Is the effect large enough to be useful? The first question concerning whether chance is a viable explanation for an observed effect is usually addressed with a null hypothesis significance test. A null hypothesis significance test tells us the probability of obtaining the effect or a more extreme effect if the null hypothesis is true. A significance test does not tell us how large the effect is or whether the effect is important or useful. Unfortunately, all too often the primary focus of research is on rejecting a null hypothesis and obtaining a small p value. The focus should be on what the data tell us about the phenomenon under investigation. This is not a new idea. Critics of significance testing have been saying it for years. For example, Frank Yates (1951), a contemporary of Ronald Fisher, observed that the use of the null hypothesis significance test

182 citations

Posted Content
TL;DR: In this paper, robust variance estimation (RVE) is proposed as a meta-analytic method for dealing with dependent effect sizes. But, traditional meta-regression models are ill-equipped to handle the complex and often unknown correlations among non-independent effect sizes, and the RVE method is not suitable for large and small sample estimators under various weighting schemes.
Abstract: Meta-regression models are commonly used to synthesize and compare effect sizes. Unfortunately, traditional meta-regression methods are ill-equipped to handle the complex and often unknown correlations among non-independent effect sizes. Robust variance estimation (RVE) is a recently proposed meta-analytic method for dealing with dependent effect sizes. The robumeta package provides functions for performing robust variance meta-regression using both large and small sample RVE estimators under various weighting schemes. These methods are distribution free and provide valid point estimates, standard errors and hypothesis tests even when the degree and structure of dependence between effect sizes is unknown.

181 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023267
2022696
2021959
2020998
20191,033
2018943