Scientific method: Statistical errors

By Regina L. Nuzzo. Nature, 2014

1,429 citations recorded by the source corpus.

Subfields: Physiology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7c2b0044fb08a19d2eedce2ba8949cda22d888b0

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Citations in the corpus

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning (2015, background): “Copyright 2016 by the author(s). mow & Marks, 2015; Nuzzo, 2014), new needs arise from deep learning tools.”
Three Recommendations for Improving the Use of p-Values (2019, background): “Nonetheless, we believe it should become part of the culture in the “post p 0.05 era” because the prior odds can matter greatly in evaluating the odds of an alternative hypothesis relative to the null hypothesis (see, e.g., Nuzzo 2014).”
Reconnecting p-Value and Posterior Probability Under One- and Two-Sided Tests (2018, background): “Nuzzo (2014) gave an editorial on why p-value alone cannot serve as adequate statistical evidence for inference.”
A new global policy regime founded on invalid statistics? Hanushek, Woessmann, PISA, and economic growth (2017, background): “This basic methodological point has been repeatedly cautioned by generations of statisticians (Berkson 1938; Bakan 1966; Cohen 1990; Thompson 1996; Lambdin 2012; Nuzzo 2014) but does not seem to be understood widely in the field of comparative education, at least as gauged by this exchange.”
Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies (2020, background): “A summary of main statistical errors frequently encountered in scientific studies is provided below (6-13):”
Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm (2015, background): “These include whether to test the assumptions underlying parametric analyses [7], when to use parametric versus nonparametric tests [8,9,10], whether to report SD versus SE for normally distributed data [5,6,8], and how to use p-values [11].”
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use (2019, background): “Model Uncertainty: Uncertainty of model parameters that best explain observed data is known to be a significant source of uncertainty in model performance (Nuzzo, 2014; Zhang et al., 2013).”
Beyond small, medium, or large: points of consideration when interpreting effect sizes (2019, background): “(Kirk, 2001, p. 213) The first question has been the topic of much debate, particularly in relation to the use and misuse of p values (Nuzzo, 2014; Trafimow & Marks, 2015; Wasserstein & Lazar, 2016).”
The need for Bayesian hypothesis testing in psychological science (2017, methodology): “The inadequacy of the null hypothesis is measured through the infamous p value (Nuzzo, 2014).”
Does Meaningful Work Explains the Relationship Between Transformational Leadership and Innovative Work Behaviour? (2019, neutral): “…the testing of the relationship between the focal constructs (transformational leadership and IWB) in an Indian context, which is in line with Nuzzo (2014), who urged that empirical investigations need multiple VIKALPA • VOLUME 44 • ISSUE 1 • JANUARY-MARCH 2019 33 replication in order to…”
What Are the Odds? A Practical Guide to Computing and Reporting Bayes Factors (2014, methodology): “It is only as p-values approach .01 that evidence starts becoming substantial, according to the calculated Bayes factors (Johnson, 2013; Nuzzo, 2014).”
The ASA Statement on p-Values: Context, Process, and Purpose (2016, background): “That same week, statistician and science writer Regina Nuzzo published an article in Nature entitled “Scientific Method: Statistical Errors” (Nuzzo 2014).”