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What to Do about the "Cult of Statistical Significance"? A Renewable Fuel Application using the Neyman-Pearson Protocol

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  • Timothy R. Wojan
  • Jason P. Brown
  • Dayton M. Lambert

Abstract

This research adapts the Neyman-Pearson testing protocol commonly used in biomedical research for ex post evaluation of the employment impacts of new ethanol bio-refineries in the U.S. Great Plains and the Midwest. By calculating the power of the test, the suggested protocol may provide policy-relevant information, even in the event of nonsignificant findings. The main obstacle to applying this protocol has been the need to posit an explicit alternative distribution, which runs counter to the empiricist tradition of mainstream econometrics. We resolve this problem by applying a data generating process with known parameters anchored to sample data to compute power.

Suggested Citation

  • Timothy R. Wojan & Jason P. Brown & Dayton M. Lambert, 2014. "What to Do about the "Cult of Statistical Significance"? A Renewable Fuel Application using the Neyman-Pearson Protocol," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 36(4), pages 674-695.
    • Handle: RePEc:oup:apecpp:v:36:y:2014:i:4:p:674-695.
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    Cited by:

    1. Jason P Brown & Dayton M Lambert & Timothy R Wojan, 2019. "The Effect of the Conservation Reserve Program on Rural Economies: Deriving a Statistical Verdict from a Null Finding," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(2), pages 528-540.
    2. Carlos J. O. Trejo-Pech & Karen L. DeLong & Dayton M. Lambert & Vasileios Siokos, 2020. "The impact of US sugar prices on the financial performance of US sugar-using firms," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 8(1), pages 1-17, December.

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