Technology, agency, critique:An interview with Claudia Aradau

info:eu-repo/semantics/publishe

Increasing the statistical power of animal experiments with historical control data

Low statistical power reduces the reliability of animal research; yet, increasing sample sizes to increase statistical power is problematic for both ethical and practical reasons. We present an alternative solution using Bayesian priors based on historical control data, which capitalizes on the observation that control groups in general are expected to be similar to each other. In a simulation study, we show that including data from control groups of previous studies could halve the minimum sample size required to reach the canonical 80% power or increase power when using the same number of animals. We validated the approach on a dataset based on seven independent rodent studies on the cognitive effects of early-life adversity. We present an open-source tool, RePAIR, that can be widely used to apply this approach and increase statistical power, thereby improving the reliability of animal experiments

Evaluation of inequality constrained hypotheses using a generalization of the AIC

In the social and behavioral sciences, it is often not interesting to evaluate the null hypothesis by means of a p-value. Researchers are often more interested in quantifying the evidence in the data (as opposed to using p-values) with respect to their own expectations represented by equality and/or inequality constrained hypotheses (as opposed to the null hypothesis). This article proposes an Akaike-type information criterion (AIC; Akaike, 1973, 1974) called the generalized order-restricted information criterion approximation (GORICA) that evaluates (in)equality constrained hypotheses under a very broad range of statistical models. The results of five simulation studies provide empirical evidence showing that the performance of the GORICA on selecting the best hypothesis out of a set of (in)equality constrained hypotheses is convincing. To illustrate the use of the GORICA, the expectations of researchers are investigated in a logistic regression, multilevel regression, and structural equation model