Abstract
Within neuroscience, psychology and neuroimaging, the most frequently used statistical approach is null-hypothesis significance testing (NHST) of the population mean. An interesting alternative is to perform NHST within individual participants and then infer, from the proportion of participants showing an effect, the prevalence of that effect in the population. We propose a novel Bayesian method to estimate such population prevalence which has several advantages over population mean NHST. First, it provides a population level inference currently missing for designs with small numbers of participants such as traditional psychophysics, animal electrophysiology and precision imaging. Second, it delivers a quantitative estimate with associated uncertainty instead of reducing an experiment to a binary inference on a population mean. Bayesian prevalence is widely applicable to a broad range of studies in neuroscience, psychology and neuroimaging. Its emphasis on detecting effects within individual participants could help address replicability issues. To facilitate the applicability of Bayesian prevalence, we provide code in Matlab, Python and R.
Competing Interest Statement
The authors have declared no competing interest.