  Determining an appropriate sample size is a critical element of study design,
and the method used to determine it should be consistent with the planned
analysis. When the planned analysis involves Bayes factor hypothesis testing,
the sample size is usually desired to ensure a sufficiently high probability of
obtaining a Bayes factor indicating compelling evidence for a hypothesis, given
that the hypothesis is true. In practice, Bayes factor sample size
determination is typically performed using computationally intensive Monte
Carlo simulation. Here, we summarize alternative approaches that enable sample
size determination without simulation. We show how, under approximate normality
assumptions, sample sizes can be determined numerically, and provide the R
package bfpwr for this purpose. Additionally, we identify conditions under
which sample sizes can even be determined in closed-form, resulting in novel,
easy-to-use formulas that also help foster intuition, enable asymptotic
analysis, and can also be used for hybrid Bayesian/likelihoodist design.
Furthermore, we show how in our framework power and sample size can be computed
without simulation for more complex analysis priors, such as
Jeffreys-Zellner-Siow priors or nonlocal normal moment priors. Case studies
from medicine and psychology illustrate how researchers can use our methods to
design informative yet cost-efficient studies.
