The Experts below are selected from a list of 182709 Experts worldwide ranked by ideXlab platform
Jan Sprenger - One of the best experts on this subject based on the ideXlab platform.
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Testing a Precise Null Hypothesis: The Case of Lindley’s Paradox
Philosophy of Science, 2013Co-Authors: Jan SprengerAbstract:Testing a point Null Hypothesis is a classical but controversial issue in statistical methodology. A prominent illustration is Lindley’s Paradox, which emerges in Hypothesis tests with large sample size and exposes a salient divergence between Bayesian and frequentist inference. A close analysis of the paradox reveals that both Bayesians and frequentists fail to satisfactorily resolve it. As an alternative, I suggest Bernardo’s Bayesian Reference Criterion: (i) it targets the predictive performance of the Null Hypothesis in future experiments; (ii) it provides a proper decision-theoretic model for testing a point Null Hypothesis; (iii) it convincingly addresses Lindley’s Paradox.
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testing a precise Null Hypothesis the case of lindley s paradox
Philosophy of Science, 2013Co-Authors: Jan SprengerAbstract:Testing a point Null Hypothesis is a classical but controversial issue in statistical methodology. A prominent illustration is Lindley’s Paradox, which emerges in Hypothesis tests with large sample size and exposes a salient divergence between Bayesian and frequentist inference. A close analysis of the paradox reveals that both Bayesians and frequentists fail to satisfactorily resolve it. As an alternative, I suggest Bernardo’s Bayesian Reference Criterion: (i) it targets the predictive performance of the Null Hypothesis in future experiments; (ii) it provides a proper decision-theoretic model for testing a point Null Hypothesis; (iii) it convincingly addresses Lindley’s Paradox.
Richard D. Morey - One of the best experts on this subject based on the ideXlab platform.
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Beyond Statistics: Accepting the Null Hypothesis in Mature Sciences:
Advances in Methods and Practices in Psychological Science, 2018Co-Authors: Richard D. Morey, Saskia Homer, Travis ProulxAbstract:Scientific theories explain phenomena using simplifying assumptions—for instance, that the speed of light does not depend on the direction in which the light is moving, or that the shape of a pea plant’s seeds depends on a small number of alleles randomly obtained from its parents. These simplifying assumptions often take the form of statistical Null hypotheses; hence, supporting these simplifying assumptions with statistical evidence is crucial to scientific progress, though it might involve “accepting” a Null Hypothesis. We review two historical examples in which statistical evidence was used to accept a simplifying assumption (that there is no luminiferous ether and that genetic traits are passed on in discrete forms) and one in which the Null Hypothesis was not accepted despite repeated failures (gravitational waves), drawing lessons from each. We emphasize the role of the scientific context in acceptance of the Null: Accepting a Null Hypothesis is never a purely statistical affair.
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Beyond statistics: accepting the Null Hypothesis in mature sciences
2018Co-Authors: Richard D. Morey, Saskia Homer, Travis ProulxAbstract:Scientific theories explain phenomena using simplifying assumptions: for instance, that the speed of light does not depend on the direction in which the light is moving, or that the height of a pea plant depends on a small number of alleles randomly obtained from its parents. The ability to support these simplifying assumptions with statistical evidence is crucial to scientific progress, though it might involve "accepting" the Null Hypothesis. We review two historical examples where statistical evidence was used to accept a simplifying assumption (rejecting the luminiferous aether and genetic theory) and one where the Null Hypothesis was not accepted in spite of repeated failures (gravitational waves), drawing lessons from each. We emphasize the role of the scientific context in the acceptance of the Null: accepting the Null is never a purely statistical affair.
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bayesian t tests for accepting and rejecting the Null Hypothesis
Psychonomic Bulletin & Review, 2009Co-Authors: Jeffrey N Rouder, Richard D. Morey, Paul L Speckman, Dongchu Sun, Geoffrey J IversonAbstract:Progress in science often comes from discovering invariances in relationships among variables; these invariances often correspond to Null hypotheses. As is commonly known, it is not possible to state evidence for the Null Hypothesis in conventional significance testing. Here we highlight a Bayes factor alternative to the conventional t test that will allow researchers to express preference for either the Null Hypothesis or the alternative. The Bayes factor has a natural and straightforward interpretation, is based on reasonable assumptions, and has better properties than other methods of inference that have been advocated in the psychological literature. To facilitate use of the Bayes factor, we provide an easy-to-use, Web-based program that performs the necessary calculations.
Ludmil B. Alexandrov - One of the best experts on this subject based on the ideXlab platform.
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Generating realistic Null Hypothesis of cancer mutational landscapes using SigProfilerSimulator.
BMC Bioinformatics, 2020Co-Authors: Erik N. Bergstrom, Mark Barnes, Inigo Martincorena, Ludmil B. AlexandrovAbstract:BACKGROUND Performing a statistical test requires a Null Hypothesis. In cancer genomics, a key challenge is the fast generation of accurate somatic mutational landscapes that can be used as a realistic Null Hypothesis for making biological discoveries. RESULTS Here we present SigProfilerSimulator, a powerful tool that is capable of simulating the mutational landscapes of thousands of cancer genomes at different resolutions within seconds. Applying SigProfilerSimulator to 2144 whole-genome sequenced cancers reveals: (i) that most doublet base substitutions are not due to two adjacent single base substitutions but likely occur as single genomic events; (ii) that an extended sequencing context of ± 2 bp is required to more completely capture the patterns of substitution mutational signatures in human cancer; (iii) information on false-positive discovery rate of commonly used bioinformatics tools for detecting driver genes. CONCLUSIONS SigProfilerSimulator's breadth of features allows one to construct a tailored Null Hypothesis and use it for evaluating the accuracy of other bioinformatics tools or for downstream statistical analysis for biological discoveries. SigProfilerSimulator is freely available at https://github.com/AlexandrovLab/SigProfilerSimulator with an extensive documentation at https://osf.io/usxjz/wiki/home/ .
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Generating realistic Null Hypothesis of cancer mutational landscapes using SigProfilerSimulator
bioRxiv, 2020Co-Authors: Erik N. Bergstrom, Mark Barnes, Inigo Martincorena, Ludmil B. AlexandrovAbstract:Performing a statistical test requires a Null Hypothesis. In cancer genomics, a key challenge is the fast generation of accurate somatic mutational landscapes that can be used as a realistic Null Hypothesis for making biological discoveries. Here we present SigProfilerSimulator, a powerful tool that is capable of simulating the mutational landscapes of thousands of cancer genomes at different resolutions within seconds. Applying SigProfilerSimulator to 2,144 whole-genome sequenced cancers reveals: (i) that most doublet base substitutions are not due to two adjacent single base substitutions but likely occur as single genomic events; (ii) that an extended sequencing context of +/-2bp is required to more completely capture the patterns of substitution mutational signatures in human cancer; (iii) information on false-positive discovery rate of commonly used bioinformatics tools for detecting driver genes. SigProfilerSimulator9s breadth of features allows one to construct a tailored Null Hypothesis and use it for evaluating the accuracy of other bioinformatics tools or for downstream statistical analysis for biological discoveries.
Cheryl Marie Webster - One of the best experts on this subject based on the ideXlab platform.
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sentence severity and crime accepting the Null Hypothesis
Crime and Justice, 2003Co-Authors: Anthony N Doob, Cheryl Marie WebsterAbstract:The literature on the effects of sentence severity on crime levels has been reviewed numerous times in the past twenty-five years. Most reviews conclude that there is little or no consistent evidence that harsher sanctions reduce crime rates in Western populations. Nevertheless, most reviewers have been reluctant to conclude that variation in the severity of sentence does not have differential deterrent impacts. A reasonable assessment of the research to date-with a particular focus on studies conducted in the past decade-is that sentence severity has no effect on the level of crime in society. It is time to accept the Null Hypothesis.
Neil Thomason - One of the best experts on this subject based on the ideXlab platform.
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impact of criticism of Null Hypothesis significance testing on statistical reporting practices in conservation biology
Conservation Biology, 2006Co-Authors: Fiona Fidler, Mark A Burgman, Geoff Cumming, Robert Buttrose, Neil ThomasonAbstract:Over the last decade, criticisms of Null-Hypothesis significance testing have grown dramatically, and several alternative practices, such as confidence intervals, information theoretic, and Bayesian methods, have been advocated. Have these calls for change had an impact on the statistical reporting practices in conservation biology? In 2000 and 2001, 92% of sampled articles in Conservation Biology and Biological Conservation reported results of Null-Hypothesis tests. In 2005 this figure dropped to 78%. There were corresponding increases in the use of confidence intervals, information theoretic, and Bayesian techniques. Of those articles reporting Null-Hypothesis testing—which still easily constitute the majority—very few report statistical power (8%) and many misinterpret statistical nonsignificance as evidence for no effect (63%). Overall, results of our survey show some improvements in statistical practice, but further efforts are clearly required to move the discipline toward improved practices.
