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Statistical Non-Significance in Empirical Economics

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  • Alberto Abadie

Abstract

Significance tests are probably the most common form of inference in empirical economics, and significance is often interpreted as providing greater informational content than non-significance. In this article we show, however, that rejection of a point null often carries very little information, while failure to reject may be highly informative. This is particularly true in empirical contexts that are typical and even prevalent in economics, where data sets are large (and becoming larger) and where there are rarely reasons to put substantial prior probability on a point null. Our results challenge the usual practice of conferring point null rejections a higher level of scientific significance than non-rejections. In consequence, we advocate a visible reporting and discussion of non-significant results in empirical practice.

Suggested Citation

  • Alberto Abadie, 2018. "Statistical Non-Significance in Empirical Economics," NBER Working Papers 24403, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:24403
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    Cited by:

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    2. Christian Leuz, 2018. "Evidence-based policymaking: promise, challenges and opportunities for accounting and financial markets research," Accounting and Business Research, Taylor & Francis Journals, vol. 48(5), pages 582-608, July.
    3. Lucia Rizzica, 2018. "Raising aspirations and higher education: evidence from the UK’s Widening Participation policy," Temi di discussione (Economic working papers) 1188, Bank of Italy, Economic Research and International Relations Area.
    4. Luigi Butera & Philip Grossman & Daniel Houser & John List & Marie Villeval, 2020. "A New Mechanism to Alleviate the Crises of Confidence in Science With An Application to the Public Goods GameA Review," Working Papers halshs-02512932, HAL.
    5. Luigi Butera & Philip J. Grossman & Daniel Houser & John A. List & Marie-Claire Villeval, 2020. "A New Mechanism to Alleviate the Crises of Confidence in Science-With An Application to the Public Goods Game," NBER Working Papers 26801, National Bureau of Economic Research, Inc.
    6. Cristina Blanco-Perez & Abel Brodeur, 2020. "Publication Bias and Editorial Statement on Negative Findings," Economic Journal, Royal Economic Society, vol. 130(629), pages 1226-1247.
    7. Eszter Czibor & David Jimenez‐Gomez & John A. List, 2019. "The Dozen Things Experimental Economists Should Do (More of)," Southern Economic Journal, John Wiley & Sons, vol. 86(2), pages 371-432, October.
    8. Finigan, Duncan & Mills, Brian M. & Stone, Daniel F., 2020. "Pulling starters," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 89(C).
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    10. Brodeur, Abel & Cook, Nikolai & Heyes, Anthony, 2018. "Methods Matter: P-Hacking and Causal Inference in Economics," IZA Discussion Papers 11796, Institute of Labor Economics (IZA).
    11. Hausladen, Carina I. & Schubert, Marcel H. & Ash, Elliott, 2020. "Text classification of ideological direction in judicial opinions," International Review of Law and Economics, Elsevier, vol. 62(C).

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    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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