Arbeitspapier
A non-Bayesian approach to scientific inference on treatment-effects
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: CREMA Working Paper ; No. 2020-14
- Klassifikation
-
Wirtschaft
- Thema
-
Statistical inference
experimental science
hypothesis testing
conditional probability
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Banerjee, Subrato
Torgler, Benno
- Ereignis
-
Veröffentlichung
- (wer)
-
Center for Research in Economics, Management and the Arts (CREMA)
- (wo)
-
Zürich
- (wann)
-
2020
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Banerjee, Subrato
- Torgler, Benno
- Center for Research in Economics, Management and the Arts (CREMA)
Entstanden
- 2020
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