Statistical Significance Testing in Economics

In Conrad Heilmann & Julian Reiss (eds.), Routledge Handbook of Philosophy of Economics. Routledge (2022)
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Abstract

The origins of testing scientific models with statistical techniques go back to 18th century mathematics. However, the modern theory of statistical testing was primarily developed through the work of Sir R.A. Fisher, Jerzy Neyman, and Egon Pearson in the inter-war period. Some of Fisher's papers on testing were published in economics journals (Fisher, 1923, 1935) and exerted a notable influence on the discipline. The development of econometrics and the rise of quantitative economic models in the mid-20th century made statistical significance testing a commonplace, albeit controversial tool within economics. In the debate about significance testing, methodological controversies intertwine with epistemological issues and sociological developments. Our aim in this chapter is to expound these connections and to show how the use of, and the debate about, significance testing in economics differs from other social sciences, such as psychology.

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Author Profiles

Jan Sprenger
University of Turin
William Peden
Johannes Kepler University of Linz

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References found in this work

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Philosophy of science and the replicability crisis.Felipe Romero - 2019 - Philosophy Compass 14 (11):e12633.
P-curve: A key to the file-drawer.Uri Simonsohn, Leif D. Nelson & Joseph P. Simmons - 2014 - Journal of Experimental Psychology: General 143 (2):534-547.

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