Bayesian vs Frequentist Comparison
Overview
The Bayesian vs Frequentist Comparison tool provides a side-by-side visualization of two fundamental approaches to statistical inference. Using a simple coin flip example, this interactive tool demonstrates the philosophical and practical differences between Bayesian and Frequentist methods. Watch how the same data leads to different interpretations depending on your statistical framework.
Tips
- The Bayesian approach updates beliefs: prior + data = posterior. Your conclusion depends on your starting assumptions
- The Frequentist approach only uses the data: what’s the probability of seeing this data if the null hypothesis were true?
- Try different priors: a skeptical prior (centered at 0.5) vs an enthusiastic prior (centered away from 0.5)
- With little data, the prior matters a lot in Bayesian analysis; with lots of data, the likelihood dominates
- Bayesian gives you P(hypothesis|data), which is what we usually want; Frequentist gives P(data|hypothesis)
- The Frequentist p-value answers “how surprising is this data under H₀?” not “how likely is H₀?”
- Watch how the posterior distribution narrows as you add more data - this represents increasing certainty
- The Bayesian credible interval has a natural interpretation: “95% probability the true value is in this range”