Enhancing Precision in Human Neuroscience

Nebe, St, Reutter, M., Baker, D. H., Bölte, J., Domes, G., Gamer, M., Gärtner, A., Gießing, C., Gurr, C., Hilger, K., Jawinski, Ph., Kulke, L., Lischke, A., Markett, S, Meier. M, Merz, Ch. J., Popov, T, Puhlmann, L. M. C. Quintana, D. S., Schäfer, T., Schubert, A-L, Sperl, M. F. J. Vehlen, A., Lonsdorf, T. B., Feld, G. B.

Abstract

Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability – in science in general, but also specifically in human neuroscience – have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.