The bayestestR package has several functions to compute indices of effect existence and significance in a Bayesian framework, like p_direction() or bayesfactor_parameters().

The accuracy of these indices is affected by various sources of uncertainty, such as sample size or noise. Using the package, we have created a small animation that demontrates how new evidence updates the posterior distribution and thereby indices of existence and significance:

If you’d like to know more (statistical) details about these indices, we have recently published a paper with a simulation study (available for free!), demonstrating how such indices behave in the context of different sources of uncertainty:

• Makowski, D., Ben-Shachar, M. S., Chen, S. H., & Lüdecke, D. (2019). Indices of Effect Existence and Significance in the Bayesian Framework. Frontiers in Psychology, 10, 2767. doi: 10.3389/fpsyg.2019.02767

In this paper, you’ll also find fancy figures like this one, showing the relationship between Bayesian indices and the frequentist p-value:

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