OBJECTIVES: The objective was to conduct a simulation study to assess the impact of using "plausibly" vague priors for the estimation of the between-study heterogeneity parameter τ in a Bayesian meta-analysis. METHODS: “Plausibly” vague priors refers to the selection of a prior more fitted to the data by remaining sufficiently vague to have results driven by the data while ensuring model convergence. Several data inputs scenarios were simulated allowing variations on the overall treatment effect θ, the number of studies and the intensity of τ estimate; the coverage probability and the length of the credibility interval of the θ estimate; and the goodness-of-fit of the model. RESULTS: Thirty-two data inputs scenarios were simulated and eight different prior scenarios were compared. Overall the length of the credibility intervals for θ were broader with the random-effects model than in the fixed-effect model, however the coverage probability was better with the random-effects estimates. Regarding the mean absolute estimation error of τ, the priors using a log-normal distribution were associated with very precise estimates, especially in case of a low number of studies, whereas more vague priors resulted in biased results. CONCLUSIONS: This empirical study showed that the use of a plausibly vague prior distribution for the variance parameter can enhance the estimation of meta-analyses results, especially in a sparse data context.