• Kitanovski, S.
• Richter, S. H.
• Hoffmann, Daniel
• Waterkamp, Ch
• Kortzfleisch, V. T. Von
• Neu, Ch

Abstract

<jats:title>Abstract</jats:title><jats:p>Reduction of the numbers of laboratory animals is one of the three pillars of ethical animal research. Equivalently, information gain per animal should be maximized. A road towards this goal that is barely taken in current animal research is the more accurate statistical modeling of experiments. Here we show for a typical experiment (“open field test”) with outcomes that are non-normally distributed count data, how this can be implemented and what information gain is achieved. We contrast the state of the art – the use of confidence intervals based on null-hypothesis significance testing (NHST) –, with a Bayesian approach with the same underlying normal model, and a Bayesian approach with a more accurate negative binomial model. We find that the more accurate model leads to a marked improvement of knowledge gained with the experiment, especially for small sample sizes. As experimental data that violate assumptions of simple, conventional models are frequent, our findings have wider implications.</jats:p>

Topics

• impedance spectroscopy
• experiment