Retrospectives: Guinnessometrics: The Economic Foundation of "Student's" t
Stephen T. Ziliak
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| Article Citation |
Ziliak, Stephen T.. 2008. "Retrospectives: Guinnessometrics: The Economic Foundation of "Student's" t." Journal of Economic Perspectives, 22(4): 199–216.
DOI:10.1257/jep.22.4.199
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| Abstract |
In economics and other sciences, "statistical significance" is by custom, habit, and education a necessary and sufficient condition for proving an empirical result. The canonical routine is to calculate what's called a t-statistic and then to compare its estimated value against a theoretically expected value of it, which is found in "Student's" t table. A result yielding a t-value greater than or equal to about 2.0 is said to be "statistically significant at the 95 percent level." Alternatively, a regression coefficient is said to be "statistically significantly different from the null, p ≤ .05." Canonically speaking, if a coefficient clears the 95 percent hurdle, it warrants additional scientific attention. If not, not. The first presentation of "Student's" test of significance came a century ago in 1908, in "The Probable Error of a Mean," published by an anonymous "Student." The author's commercial employer required that his identity be shielded
from competitors, but we have known for some decades that the article was written by William Sealy Gosset (1876-1937), whose entire career was spent at Guinness's brewery in Dublin, where Gosset was a master brewer and experimental scientist. Perhaps surprisingly, the ingenious "Student" did not give a hoot for a single finding of "statistical" significance, even at the 95 percent level of significance as established by his own tables. Beginning in 1904, "Student," who was a businessman besides a scientist, took an economic approach to the logic of uncertainty, arguing finally that statistical significance is "nearly valueless" in itself.
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| Authors |
Ziliak, Stephen T. (Roosevelt U)
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| JEL Classifications |
B16: History of Thought: Quantitative and Mathematical
C00: Mathematical and Quantitative Methods: General
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(pp. 3 - 26)