Statistical methods that assign probabilities or distributions to events (such as rain tomorrow) or parameters (such as a population mean) based on experience or best guesses before experimentation and data collection. These methods apply Bayes’ theorem to revise the probabilities and distributions after obtaining experimental data.

Bayesian has the advantage that results are easier to interpret and understand by non stats folks (none of this p-value or hypothesis testing nonsense), as results are read out like “The probability there is a real effect is 83%” or “The probability we’re making a decision that hurts revenue is 14%”. However to do this, the math is a lot more complicated and subjective.