Direct application of Bayes theorem or more generally Bayesian inference (made easy now through probabilistic programming languages like stan or pymc3) can solv

Direct application of Bayes theorem or more generally Bayesian inference (made easy now through probabilistic programming languages like stan or pymc3) can solve all of the issues that I read on this site. In particular, the issue raised here[1] is about comparing estimates when the sample sizes are very different. Hierarchical partial pooling, can be used to accurately compare groups when the samples sizes vary widely, even when some groups have zero observations. Here is an example talking about baseball batting averages[2]. [1] https://www.statisticsdonewrong.com/regression.html#little-extremes https://www.statisticsdonewrong.com/regression.html#little-e... [2] https://docs.pymc.io/notebooks/hierarchical_partial_pooling.html https://docs.pymc.io/notebooks/hierarchical_partial_pooling....