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Direct linkFiedler, Klaus; Kareev, Yaakov – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2011
On the basis of earlier findings, we (Fiedler & Kareev, 2006) presented a statistical decision model that explains the conditions under which small samples of information about choice alternatives inform more correct choices than large samples. Such a small-sample advantage (SSA) is predicted for choices, not estimations. It is contingent on high…
Descriptors: Sample Size, Information Theory, Prediction, Selection
Cahan, Sorel – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2010
Fiedler and Kareev (2006) showed that small samples can, in principle, outperform large samples in terms of the quality of contingency-based binary choice. The 1st part of this comment critically examines these authors' claim that this small sample advantage (SSA) contradicts Bernoulli's law of large numbers and concludes that this claim is…
Descriptors: Decision Making, Selection, Etiology, Statistics
Mackintosh, N. J. – Intelligence, 2007
Mackintosh and Bennett [Mackintosh, N. J., Bennett, E. S. (2005). What do Raven's Matrices measure? An analysis in terms of sex differences. "Intelligence, 33," 663-674] reported that male students obtained higher scores than females on Raven's items that required for their solution addition/subtraction or distribution of two rules, but…
Descriptors: Gender Differences, Sample Size, Scores, Test Reliability
Kraemer, Helena Chmura – Psychological Methods, 2005
R. Rosenthal and D. B. Rubin (2003) proposed an effect size, r-sub(equivalent), to be used when (a) only sample size and p values are known for a study, (b) there are no generally accepted effect size indicators, or (c) sample sizes are so small or the data so non-normal that the directly computed effect sizes would be more misleading than the…
Descriptors: Effect Size, Sample Size, Reader Response, Criticism
