The Bayesian Advantage: Why We Don't Use Traditional A/B Tests
If you've ever tried to run a traditional A/B test on a product with low traffic, you know the frustration: you wait weeks, the results come back "inconclusive," and you're back to square one.
Traditional A/B testing was designed for products with millions of monthly users. It does not work for solo founders doing $500–$10k MRR.
The Problem With Frequentist Testing
Traditional A/B tests use frequentist statistics. This framework requires pre-determined sample sizes, no peeking at results, and lots of data — typically 200–1000 conversions per variant.
If you have 50 sales a month, a traditional A/B test would take 6–12 months to reach significance. That's useless.
What Bayesian Testing Does Differently
Bayesian statistics answers: "Given the data I've seen so far, what is my best estimate of which price is better, and how confident am I?"
- Updates continuously as new data arrives
- Expresses uncertainty directly as a probability
- Works with small samples — even 20–30 conversions per variant give meaningful signal
How PricingSim's Engine Works
We model each price point's conversion rate as a Beta distribution. When a new sale comes in, we update the distribution using Bayes' theorem. Over time, the distributions either converge (similar performance) or diverge (one is clearly better).
We also account for time effects, cohort effects, and seasonality that could confound results.
The Bottom Line
For low-traffic, high-intent products, Bayesian testing is the right tool. You get actionable insights in weeks, not months, with honest uncertainty quantification.