Bayesian Testing
An experimentation approach that uses Bayes' theorem to calculate the probability that one variant is better than another, incorporating prior knowledge and updating beliefs as data accumulates.
Also known as: Bayesian A/B testing, Bayesian experimentation
Why It Matters
Bayesian testing answers the question teams actually want answered: "What is the probability that variant B is better than variant A?" Frequentist methods answer a different, less intuitive question about p-values and null hypotheses. The Bayesian approach provides a direct probability (e.g., "there is a 94% chance variant B is better") that is easier for non-statisticians to understand and act on.
Bayesian methods also handle early stopping more gracefully. Unlike frequentist tests where peeking inflates false positive rates, Bayesian posterior probabilities can be checked at any time without requiring correction. This makes them well-suited for organizations that need to make decisions quickly.
The ability to incorporate prior knowledge is another advantage. If you have strong historical data about your conversion rate, Bayesian priors let you include that information, potentially reaching conclusions faster. However, this strength is also a risk - poorly chosen priors can bias results.
Industry Applications
A subscription box company uses Bayesian testing for pricing experiments. After 5,000 subscribers per variant, the Bayesian analysis reports a 97% probability that the $39.99 price point generates more total revenue than $44.99, with an expected loss of only 0.2% if they choose $39.99. The team ships confidently.
A project management tool uses Bayesian testing to compare four different free trial lengths (7, 14, 21, and 30 days). The Bayesian analysis naturally handles the four-way comparison and identifies the 14-day trial as having an 88% probability of being the best for trial-to-paid conversion.
How to Track in KISSmetrics
Some analytics platforms offer Bayesian test analysis. When using KISSmetrics for experiments, you can apply Bayesian analysis to your results by exporting the raw data and using statistical tools. The key output to look for is the posterior probability that each variant is the best performer and the expected loss (risk) of choosing each variant.
Common Mistakes
- -Choosing overly strong priors that bias results toward preexisting beliefs, especially with small sample sizes
- -Using Bayesian testing as an excuse to stop tests too early with insufficient data
- -Not specifying a loss threshold (the maximum acceptable risk of choosing the wrong variant)
- -Misinterpreting posterior probabilities as certainties - "92% probability B is better" still means an 8% chance it is not
- -Using uniform (uninformative) priors when strong historical data is available, wasting information
Pro Tips
- +Set a decision rule before the test: "We will ship variant B when the probability of it being better exceeds 95% AND the expected loss of choosing it falls below 0.1%"
- +Use weakly informative priors based on historical data rather than completely uninformative priors
- +Report results as probability of being best, expected lift, and expected loss - this trio gives stakeholders everything they need for a decision
- +Bayesian methods shine when you need to compare more than two variants simultaneously, as they naturally handle multiple comparisons
Related Terms
Frequentist Testing
The traditional statistical approach to A/B testing that evaluates results by calculating how likely the observed data would be if there were no real difference between variants, using p-values and confidence intervals.
Hypothesis Testing
A statistical method used to determine whether observed differences in data - such as a higher conversion rate in a test variant - are likely real or could have occurred by random chance.
P-Value
The probability of observing a result as extreme as the one measured, assuming the null hypothesis is true. A small p-value (typically below 0.05) suggests the observed difference is unlikely due to chance alone.
Confidence Interval
A range of values that likely contains the true effect of a change, calculated from experiment data. A 95% confidence interval means that if the experiment were repeated many times, 95% of the calculated intervals would contain the true value.
Statistical Power
The probability that a test will correctly detect a real effect when one exists, typically set at 80% as a minimum standard. Higher power means a lower chance of missing genuine improvements.
See Bayesian Testing in action
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