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Beyond P-Value-Obsession: When are Statistical Hypothesis Tests Required and Appropriate?

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  • Margarian, Anne

Abstract

Complementing more specific “p-value discussions”, this paper presents fundamental arguments for when null hypothesis statistical significance tests (NHST) are required and appropriate. The arguments, which are paradigmatic rather than technical, are operationalized and broken down to the extent that their logic can be mapped into a decision tree for the use of NHST. We derive a perspective that does not ban p-values but proposes to minimize their use. P-values will become rather rare in (agricultural) economics if they are not applied in any cases, where the conditions for their proper use are violated or where their use is not appropriate or required in order to answer the questions asked of the data. The accompanying shift from prioritising inferential statistics to recognising the value of descriptive statistics requires not only a change in entrenched habits of thought. This shift also has the potential to trigger changes in the research processes and in the evaluation of new approaches within the disciplines.

Suggested Citation

  • Margarian, Anne, 2022. "Beyond P-Value-Obsession: When are Statistical Hypothesis Tests Required and Appropriate?," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 71(04), January.
    • Handle: RePEc:ags:gjagec:343323
    DOI: 10.22004/ag.econ.343323
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    Cited by:

    1. Margarian, Anne, 2024. "Analysing evolutionary growth regimes of regional economies and transformative shocks: Proposal for a regression-based counterfactual simulation approach to local inter-industry structural change," Structural Change and Economic Dynamics, Elsevier, vol. 70(C), pages 18-32.

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    Research Methods/ Statistical Methods;

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