  Scientific claims gain credibility by replicability, especially if
replication under different circumstances and varying designs yields equivalent
results. Aggregating results over multiple studies is, however, not
straightforward, and when the heterogeneity between studies increases,
conventional methods such as (Bayesian) meta-analysis and Bayesian sequential
updating become infeasible. *Bayesian Evidence Synthesis*, built upon the
foundations of the Bayes factor, allows to aggregate support for conceptually
similar hypotheses over studies, regardless of methodological differences. We
assess the performance of Bayesian Evidence Synthesis over multiple effect and
sample sizes, with a broad set of (inequality-constrained) hypotheses using
Monte Carlo simulations, focusing explicitly on the complexity of the
hypotheses under consideration. The simulations show that this method can
evaluate complex (informative) hypotheses regardless of methodological
differences between studies, and performs adequately if the set of studies
considered has sufficient statistical power. Additionally, we pinpoint
challenging conditions that can lead to unsatisfactory results, and provide
suggestions on handling these situations. Ultimately, we show that Bayesian
Evidence Synthesis is a promising tool that can be used when traditional
research synthesis methods are not applicable due to insurmountable
between-study heterogeneity.
