Stephen M. Stigler. &lt;italic&gt;The History of Statistics: The Measurement of Uncertainty before 1900&lt;/italic&gt;. Cambridge: Belknap Press of Harvard University Press. 1986. Pp. ix, 410

Becher, Harvey W.

doi:10.1086/ahr/93.4.1019

The American Historical Review

1988

DOI: 10.1086/ahr/93.4.1019

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Stephen M. Stigler. <italic>The History of Statistics: The Measurement of Uncertainty before 1900</italic>. Cambridge: Belknap Press of Harvard University Press. 1986. Pp. ix, 410

Harvey W. Becher

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Cited by 11 publications

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Before p < 0.05 to Beyond p < 0.05: Using History to Contextualize p -Values and Significance Testing

Kennedy‐Shaffer

2019

The American Statistician

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As statisticians and scientists consider a world beyond p < 0.05, it is important to not lose sight of how we got to this point. Although significance testing and p-values are often presented as prescriptive procedures, they came about through a process of refinement and extension to other disciplines. Ronald A. Fisher and his contemporaries formalized these methods in the early twentieth century and Fisher's 1925 Statistical Methods for Research Workers brought the techniques to experimentalists in a variety of disciplines. Understanding how these methods arose, spread, and were argued over since then illuminates how p < 0.05 came to be a standard for scientific inference, the advantage it offered at the time, and how it was interpreted. This historical perspective can inform the work of statisticians today by encouraging thoughtful consideration of how their work, including proposed alternatives to the p-value, will be perceived and used by scientists. And it can engage students more fully and encourage critical thinking rather than rote applications of formulae. Incorporating history enables students, practitioners, and statisticians to treat the discipline as an ongoing endeavor, crafted by fallible humans, and provides a deeper understanding of the subject and its consequences for science and society.

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Before p < 0.05 to Beyond p < 0.05: Using History to Contextualize p -Values and Significance Testing

Kennedy‐Shaffer

2019

The American Statistician

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Was Quetelet’s Average Man Normal?

Gallagher

2020

The American Statistician

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A New Coefficient of Correlation

Chatterjee

2020

Journal of the American Statistical Association

180

168

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Is it possible to define a coefficient of correlation which is (a) as simple as the classical coefficients like Pearson's correlation or Spearman's correlation, and yet (b) consistently estimates some simple and interpretable measure of the degree of dependence between the variables, which is 0 if and only if the variables are independent and 1 if and only if one is a measurable function of the other, and (c) has a simple asymptotic theory under the hypothesis of independence, like the classical coefficients? This article answers this question in the affirmative, by producing such a coefficient. No assumptions are needed on the distributions of the variables. There are several coefficients in the literature that converge to 0 if and only if the variables are independent, but none that satisfy any of the other properties mentioned above.2010 Mathematics Subject Classification. 62H20, 62H15.

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Stephen M. Stigler. <italic>The History of Statistics: The Measurement of Uncertainty before 1900</italic>. Cambridge: Belknap Press of Harvard University Press. 1986. Pp. ix, 410

Cited by 11 publications

References 0 publications

Before p < 0.05 to Beyond p < 0.05: Using History to Contextualize p -Values and Significance Testing

Before p < 0.05 to Beyond p < 0.05: Using History to Contextualize p -Values and Significance Testing

Was Quetelet’s Average Man Normal?

A New Coefficient of Correlation

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