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After Statistics Reform: Should We Still Teach Significance Testing?
, 2014 In the longer term null hypothesis significance testing (NHST) will disappear because p-values are not informative and not replicable. As with any reform, the question can be asked whether we should continue to teach the procedures of abolished routines (
Hak, A
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Public Health and Toxicology, 2021 Null hypothesis significance testing (NHST) text was once widely popular and almost systematically used for the identification of causal relations and for risk assessment in toxicology and medicine.
Silvio Roberto Vinceti +1 more
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REACT to NHST: Sensible conclusions to meaningful hypotheses [PDF]
, 2023 While Null Hypothesis Significance Testing (NHST) remains a widely used statistical tool, it suffers from several shortcomings, such as conflating statistical and practical significance, sensitivity to sample size, and the inability to distinguish ...
Izbicki, Rafael +5 more
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NHST-SR: A systematic review of NHST essays
, 2022 This OSF project contains all documentation, notes, data, and code that pertain to the systematic review of essay and opinion literature on NHST. The README.txt file contains some rudimentary explanations about what is where and what the variable names ...
Noah van Dongen
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Alternatives to P value: confidence interval and effect size [PDF]
Korean Journal of Anesthesiology, 2016 The previous articles of the Statistical Round in the Korean Journal of Anesthesiology posed a strong enquiry on the issue of null hypothesis significance testing (NHST).
Dong Kyu Lee
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Bayesian inference of population prevalence
eLife, 2021 Within neuroscience, psychology, and neuroimaging, the most frequently used statistical approach is null hypothesis significance testing (NHST) of the population mean.
Robin AA Ince +3 more
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Null regions: a unified conceptual framework for statistical inference
Royal Society Open Science, 2023 Ruling out the possibility that there is absolutely no effect or association between variables may be a good first step, but it is rarely the ultimate goal of science.
Adam H. Smiley +2 more
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Frontiers in Psychology, 2010 A statistically significant result, and a non-significant result may differ little, although significance status may tempt an interpretation of difference.
Melissa Coulson +3 more
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A logical analysis of null hypothesis significance testing using popular terminology
BMC Medical Research Methodology, 2022 Background Null Hypothesis Significance Testing (NHST) has been well criticised over the years yet remains a pillar of statistical inference. Although NHST is well described in terms of statistical models, most textbooks for non-statisticians present the
Richard McNulty
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Think like a Bayesian and avoid pitfalls from our frequentist past
Ideas in Ecology and Evolution, 2022 Bayesian inference is a powerful tool that is increasingly being used by ecologists. This is largely due to the flexibility in model specification and improvements in software that makes this tool easier to use.
Jason Doll, Zachary Feiner
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