THE PROBABLE ERROR OF A MEAN

By Student. 1908

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Citations in the corpus

Machine Learning that Matters (2012, methodology): “Methods from statistics such as the t-test (Student, 1908) are commonly used to support a conclusion about whether a given performance improvement is “signiﬁcant” or not.”
A critical assessment of connectivity measures for EEG data: A simulation study (2013, background): “The transformation is performed per z = cdf z −1 (cdf t (t,ν)), where cdf t is the cumulative distribution function (cdf) of Students t-distribution with ν degrees of freedom Gosset (1908) and cdf z −1 is the inverse of the cdf of the univariate standard normal distribution.”
Least-squares two-sample test (2011, methodology): “The t-test (Student, 1908) is a classical method for testing homogeneity, which compares the means of two Gaussian distributions with common variance.”
Final Collapse of the Neyman-Pearson Decision Theoretic Framework and Rise of the neoFisherian (2009, background): “It doubtless evolved in close conjunction with the idea of a dichotomized P scale, K. Pearson's and W. S. Gosset's (Student 1906–1907, Berkson 1942) notions about 'goodness-of-fit' tests, and Gosset's (Student 1908) original misinterpretation of P as the probability that H 0 is true.”
DWE: Discriminating Word Enumerator (2005, methodology): “The Z test of Student (1908) is given in Equation 2.”
The Scientific Method in the Science of Machine Learning (2019, methodology): “ this frameworkis the formulationof ascientiﬁc hypothesisandan expectationthatcan be falsiﬁed throughexperiments and statistical methods (Jeffrey, 1956; Popper, 1959; Lindberg, 1967; Arbuthnot, 1710; Student, 1908); failure to include these steps is likely to lead to unscientiﬁc ﬁndings. Awell-forme”
On Student's 1908 Article “The Probable Error of a Mean” (2008, unknown): “This month marks the 100th anniversary of the appearance of William Sealey Gosset’s celebrated article, “The Probable Error of a Mean” (Student 1908a).”