Inhaltszusammenfassung

In the literature on social hypothesis testing, the co-occurrence of 2 principles is often held responsible for hypothesis confirmation. The first is positive testing (e.g., looking for covert rather than overt aggression when testing the stereotype that female aggression is covert), and the second is a cooperative social environment that will often acquiesce and provide positive answers (i.e., positive examples for covert female aggression). However, it is argued that the co-occurrence of 1-...In the literature on social hypothesis testing, the co-occurrence of 2 principles is often held responsible for hypothesis confirmation. The first is positive testing (e.g., looking for covert rather than overt aggression when testing the stereotype that female aggression is covert), and the second is a cooperative social environment that will often acquiesce and provide positive answers (i.e., positive examples for covert female aggression). However, it is argued that the co-occurrence of 1-sided questions and confirming answers does not logically verify a hypothesis. A theoretical framework is presented that explains why a constant ratio of confirming to disconfirming evidence has more impact when based on a large than on a small sample of observations. In 2 experiments, a constant affirmation rate led to auto-verification of the hypothesis that was represented by the larger sample. The enhanced significance of large samples is shown to be independent of stereotypical expectancies and unconfounded with diagnosticity. (PsycINFO Database Record (c) 2016 APA, all rights reserved)» weiterlesen» einklappen

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