von Stephan Nebe ; Mario Reuter ; Daniel H. Baker ; Jens Bölte ; Gregor Domes ; Matthias Gamer ; Anne Gärtner ; Carsten Giessing ; Caroline Gurr ; Kirsten Hilger ; Philippe Jawinski ; Louisa Kulke
experimental methods; generalizability; human neuroscience; precision; reliability; sample size
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.
von Lisa Dandolo ; Klaus Telkmann ; Christina Hartig ; Sophie Horstmann ; Sara Pedron ; Lars Schwettmann ; Peter Selsam ; Alexandra Schneider ; Gabriele Bolte
Institute of Electrical and Electronics Engineers IEEE journal of translational engineering in health and medicine [New York, NY] : IEEE, 2013 11(2023), Seite 479-486 Online-Ressource