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A Non-Bayesian Approach to Scientific Inference on Treatment-Effects

Author

Listed:
  • Subrato Banerjee
  • Benno Torgler
Abstract
Because the use of p-values in statistical inference often involves the rejection of a hypothesis on the basis of a number that itself assumes the hypothesis to be true, many in the scientific community argue that inference should instead be based on the hypothesis’ actual probability conditional on supporting data. In this study, therefore, we propose a non-Bayesian approach to achieving statistical inference independent of any prior beliefs about hypothesis probability, which are frequently subject to human bias. In doing so, we offer an important statistical tool to biology, medicine, and any other academic field that employs experimental methodology.

Suggested Citation

  • Subrato Banerjee & Benno Torgler, 2020. "A Non-Bayesian Approach to Scientific Inference on Treatment-Effects," CREMA Working Paper Series 2020-14, Center for Research in Economics, Management and the Arts (CREMA).
    • Handle: RePEc:cra:wpaper:2020-14
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    References listed on IDEAS

    as
    1. Regina Nuzzo, 2014. "Scientific method: Statistical errors," Nature, Nature, vol. 506(7487), pages 150-152, February.
    2. Subrato Banerjee, 2015. "Power analysis and sample sizes: A Binding frontier approach," Discussion Papers 15-04, Indian Statistical Institute, Delhi.
    Full references (including those not matched with items on IDEAS)

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    Keywords

    Statistical inference; experimental science; hypothesis testing; conditional probability;
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