Can p-values be meaningfully interpreted without random sampling?

Hirschauer, Norbert; Grüner, Sven; Mußhoff, Oliver; Becker, Claudia; Jantsch, Antje

doi:10.31235/osf.io/yazr8

Cited by 8 publications

(10 citation statements)

References 23 publications

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“…A fundamental objection against statistical inference is raised by Hirschauer et al (2020) in case of full population surveys. They argue that displaying p-values does not make sense, because there is nothing to infer, and sampling error does not exist.…”

Section: Erroneous Applications Of Significance Testsmentioning

confidence: 99%

The Replicability Crisis and the p-Value Debate – what Are the Consequences for the Agricultural and Food Economics Community?

Heckelei¹,

Hüttel²,

Rommel³

et al. 2022

Preprint

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A vivid debate is ongoing in the scientific community about statistical malpractice and the related publication bias. No general consensus exists on the consequences and this is reflected in heterogeneous rules defined by scientific journals on the use and reporting of statistical inference. This paper aims at discussing how the debate is perceived by the agricultural economics community and implications for our roles as researchers, contributors to the scientific publication process, and teachers. We start by summarizing the current state of the p-value debate and the replication crisis, and commonly applied statistical practices in our community. This is followed by motivation, design, results and discussion of a survey on statistical knowledge and practice among the researchers in the agricultural economics community in Austria, Germany and Switzerland. We conclude that beyond short-term measures like changing rules of reporting in publications, a cultural change regarding empirical scientific practices is needed that stretches across all our roles in the scientific process. Acceptance of scientific work should largely be based on the theoretical and methodological rigor and where the perceived relevance arises from the questions asked, the methodology employed, and the data used but not from the results generated. Revised and clear journal guidelines, the creation of resources for teaching and research, and public recognition of good practice are suggested measures to move forward.

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Section: Erroneous Applications Of Significance Testsmentioning

confidence: 99%

The Replicability Crisis and the p-Value Debate – what Are the Consequences for the Agricultural and Food Economics Community?

Heckelei¹,

Hüttel²,

Rommel³

et al. 2022

Preprint

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“…It is a means to the end of evaluating a single study's knowledge contribution given the uncertainty (noise) caused by random sampling error (note that I do not talk here about randomised controlled trials). Therefore, statistical inference requires the sample under study to be a random sample 8 . Or more pointedly: statistical assumptions are empirical commitments and acting as if one obtained data through random sampling does not create a random sample 9 …”

Section: Question 2: Making Inferencesmentioning

confidence: 99%

Unanswered Questions in the p-value Debate

Hirschauer

2022

Significance

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“…They are devoid of inferential meaning (except when deviations from random sampling are adequately corrected within a sample selection model). 2 More generally speaking, all statistical inferential procedures based on the standard error -including statistical significance tests -are inappropriate for making sample-to-population inferences when studies are based on non-random samples.…”

Section: Statistics Are Sample Quantitiesmentioning

confidence: 99%