Topic
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.
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TL;DR: By this method, no lumping of data is required, and the accuracy of the estimate of alpha (i.e., a type 1 error) depends only on the number of randomizations of the original data set.
Abstract: Significance levels obtained from a x2 contingency test are suspect when sample sizes are small. Traditionally this has meant that data must be combined. However, such an approach may obscure heterogeneity and hence potentially reduce the power of the statistical test. In this paper, we present a Monte Carlo solution to this problem: by this method, no lumping of data is required, and the accuracy of the estimate of c1 (i.e., a type 1 error) depends only on the number of randomizations of the original data set. We illustrate this technique with data from mtDNA studies, where numerous genotypes are often observed and sample sizes are relatively small.
948 citations
TL;DR: Ten prominent advantages of the Bayesian approach are outlined, and several objections to Bayesian hypothesis testing are countered.
Abstract: Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and p values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete opportunities for pragmatic researchers. For instance, Bayesian hypothesis testing allows researchers to quantify evidence and monitor its progression as data come in, without needing to know the intention with which the data were collected. We end by countering several objections to Bayesian hypothesis testing. Part II of this series discusses JASP, a free and open source software program that makes it easy to conduct Bayesian estimation and testing for a range of popular statistical scenarios (Wagenmakers et al. this issue).
940 citations
TL;DR: In this article, a synthesis of Bayesian and sample-reuse approaches to the problem of high structure model selection geared to prediction is presented. But this approach is not suitable for high-dimensional models.
Abstract: This article offers a synthesis of Bayesian and sample-reuse approaches to the problem of high structure model selection geared to prediction. Similar methods are used for low structure models. Nested and nonnested paradigms are discussed and examples given.
940 citations
TL;DR: In this article, the consequences of prejudice against accepting the null hypothesis through a mathematical model intended to stimulate the research-publication process and case studies of apparent erroneous rejections of the null hypotheses in published psychological research were examined through a questionnaire survey of a sample of social psychologists.
Abstract: The consequences of prejudice against accepting the null hypothesis were examined through (a) a mathematical model intended to stimulate the research-publication process and (b) case studies of apparent erroneous rejections of the null hypothesis in published psychological research. The input parameters for the model characterize investigators' probabilities of selecting a problem for which the null hypothesis is true, of reporting, following up on, or abandoning research when data do or do not reject the null hypothesis, and they characterize editors' probabilities of publishing manuscripts concluding in favor of or against the null hypothesis. With estimates of the input parameters based on a questionnaire survey of a sample of social psychologists, the model output indicates a dysfunctional research-publication system. Particularly, the model indicates that there may be relatively few publications on problems for which the null hypothesis is (at least to a reasonable approximation) true, and of these, a high proportion will erroneously reject the null hypothesis. The case studies provide additional support for this conclusion. Accordingly, it is concluded that research traditions and customs of discrimination against accepting the null hypothesis may be very detrimental to research progress. Some procedures that can help eliminate this bias are prescribed.
937 citations
TL;DR: The minimalist hypothesis is that readers do not automatically construct inferences to fully represent the situation described by a text, and it is argued that automatically encoded minimalist inferences provide the basic representation of textual information from which more goal-directed, purposeful inferences are constructed.
Abstract: Most current theories of text processing assume a constructionist view of inference processing. In this article, an alternative view is proposed, labeled the minimalist hypothesis. According to this hypothesis, the only inferences that are encoded automatically during reading are those that are based on easily available information, either from explicit statements in the text or from general knowledge, and those that are required to make statements in the text locally coherent. The minimalist hypothesis is shown to be supported by previous research and by the results of several new experiments. It is also argued that automatically encoded minimalist inferences provide the basic representation of textual information from which more goal-directed, purposeful inferences are constructed.
936 citations