Bayesian vs frequentist A/B testing for marketers

What does each approach report?

A frequentist test reports a p-value and a confidence interval for the difference, and asks you to decide the sample size before you start and read the result only at the end. Its guarantee is about the procedure: if you follow it, false winners appear at most the rate you chose, usually 5 percent.

A Bayesian test starts from a prior belief about likely conversion rates, updates it with the data, and reports a posterior: the probability that each version is best and the likely size of the difference. Its statements are about this test, which is why they read more naturally.

Is Bayesian testing safe to check every day?

Safer than a frequentist test read daily, but not free. Stopping the moment the probability crosses 95 percent still produces more false winners than the number suggests, because random swings cross the line too. Good practice is the same either way: set a minimum sample and a decision rule before starting.

Which suits a continuously running system?

Bayesian methods fit naturally, because a system that runs for months is always updating its beliefs and needs a current answer rather than a final one. Bandit algorithms such as Thompson sampling are built on exactly that updating. But a continuous system still needs guard rails: a minimum sample per option, a threshold for promotion, and a fixed control group to keep the comparison honest.

IntellQ uses a probability that the change is better than control, 95 percent by default, and will not promote a change until each arm has at least 200 visitors, with the threshold and minimum adjustable in the workspace's guardrails.

Does the choice of prior matter?

With a little data, yes. With a lot, hardly at all. Most commercial tools use a weak prior that lets the data dominate quickly. Be suspicious of strong priors you did not choose, since they can make a small test look more certain than it is.

• Ask what prior your tool uses
• Prefer weak priors unless you have good historical data
• Check results at the minimum sample, not before

So which should a marketer use?

Whichever your tool implements, used with discipline. The bigger risks in experimentation are not the statistical school but the habits around it: tests that are too small, results read too early, and lifts reported without a range. Fix those and either approach will serve you well.

What does each say about the same result?

Suppose a test ends with control converting at 2.4 percent and the new version at 2.8 percent, on 20,000 visitors each. A frequentist report might say the difference is significant at the 95 percent level, with a confidence interval for the relative lift from about 4 to 30 percent. A Bayesian report on the same data, with a weak prior, would say there is roughly a 99 percent probability the new version is better, with a most likely lift near 17 percent.

Both describe the same evidence. The Bayesian sentence is easier to act on, and the frequentist interval is a useful reminder of how wide the plausible range still is. Reporting the range under either approach is what keeps decisions honest.

In practice, pick the method your tool uses, learn exactly what its headline number means, and write that definition at the top of every result you share. Most confusion in experiment reviews comes from two people reading the same number as two different claims.