Judge for yourself: reply to Evans and Buehner (2011)
In their comment, Evans and Buehner (2011) maintained that Fiedler and Kareev's (2006) conclusion that decision quality does not always increase with the size of information sample is wrong in every respect. They claimed, first, that the decision model proposed by Fiedler and Kareev is normativ...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2011
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| In: |
Journal of experimental psychology. Learning, memory, and cognition
Year: 2011, Jahrgang: 37, Heft: 6, Pages: 1595-1598 |
| ISSN: | 1939-1285 |
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| Verfasserangaben: | Yaakov Kareev, Klaus Fiedler |
| Zusammenfassung: | In their comment, Evans and Buehner (2011) maintained that Fiedler and Kareev's (2006) conclusion that decision quality does not always increase with the size of information sample is wrong in every respect. They claimed, first, that the decision model proposed by Fiedler and Kareev is normatively incorrect and is not supported by earlier findings; second, that Fiedler and Kareev misinterpreted or misrepresented their own data, which show in fact a large-sample advantage; and third, that it is not true that small samples lead to clear data; rather, when clear data are observed people make do with small samples. In this rebuttal, we refute all these claims. First, the issue is whether the model is descriptively, not normatively, correct. Furthermore, earlier data are commensurate with our, not Evans and Buehner's, model. Second, our data do support our conclusion; it is Evans and Buehner's dismissal of some of our data that led them to their conclusions. Finally, Evans and Buehner's third point is discussed explicitly and at length in the original article. However, whereas Evans and Buehner only reiterate this point (and present it as novel), we continue from there and show that small samples are likely to result in clear data. (PsycINFO Database Record (c) 2016 APA, all rights reserved) |
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| Beschreibung: | Gesehen am 11.07.2022 |
| Beschreibung: | Online Resource |
| ISSN: | 1939-1285 |