Predict experiment duration using Bayesian evidence and traffic assumptions. Compare variants, priors, and conversion goals. Make faster launch decisions with practical probability based planning.
| Scenario | Baseline Rate | Expected Lift | Daily Visitors | Split | Posterior Target | Prior Strength | Estimated Days |
|---|---|---|---|---|---|---|---|
| Recommendation model update | 8.00% | 12.00% | 5000 | 50 / 50 | 95.00% | 100 | About 7 to 14 days |
| Prompt variant test | 4.20% | 8.00% | 18000 | 60 / 40 | 97.50% | 80 | About 6 to 12 days |
| Ranking change on low traffic page | 2.10% | 5.00% | 2400 | 50 / 50 | 95.00% | 50 | About 30 to 60 days |
This tool uses a practical Bayesian planning approximation for two variants.
1. Baseline rate: p0 = baseline_rate / 100
2. Variant rate: p1 = p0 × (1 + expected_lift / 100)
3. Prior for each arm: Beta(1 + m × p0, 1 + m × (1 - p0)), where m is prior strength.
4. Posterior mean for each arm: alpha / (alpha + beta)
5. Posterior variance for each arm: (alpha × beta) / (((alpha + beta)^2) × (alpha + beta + 1))
6. Difference model: diff = mean_variant - mean_control
7. Probability variant beats control is approximated from a normal model on the posterior difference.
8. Duration equals the first evaluation day where sample, conversion, and posterior targets are all satisfied.
A Bayesian test duration calculator helps teams estimate how long an experiment should run. That matters in AI and Machine Learning work. Product teams test ranking models, prompts, onboarding flows, and recommendation systems. Ending too early increases risk. Running too long wastes traffic, time, and engineering effort.
This calculator combines baseline conversion rate, expected lift, traffic split, and prior strength. It then estimates the sample needed per variant. Next, it converts that sample into days. It also checks minimum samples, minimum conversions, and a target posterior probability. The result gives a practical runtime estimate for a two variant Bayesian experiment.
Bayesian planning is useful because it matches real decision making. Teams often ask, “What is the probability the new version is better?” That is a Bayesian question. Instead of focusing only on fixed horizon significance, Bayesian testing uses priors and posterior updates. This makes the framework flexible for product optimization and model iteration.
Low baseline conversion rates usually extend the test. Small expected lifts also extend the test. Higher posterior certainty targets need more evidence. Uneven traffic splits slow one arm and can delay completion. Stronger priors can shorten the estimate when prior information is trustworthy. Lower real traffic also increases total days.
AI teams use Bayesian experiments in many places. They compare chatbot prompts. They test model assisted search. They evaluate recommendation policies. They monitor checkout ranking, fraud rules, and retention flows. A duration estimate helps teams plan launch calendars, stakeholder reviews, and experiment queues with less guesswork.
This tool gives an informed estimate, not a guarantee. Real results still depend on actual behavior, seasonality, tracking quality, and data drift. Use it before launch. Recheck assumptions when traffic changes. Pair it with sound metrics and clear stop rules. That creates faster, safer, and more reliable experiment decisions. It is helpful when experiment portfolios are large and each week affects revenue, learning speed, planning quality, and confidence.
It estimates how many days a Bayesian A/B test may need before the variant has enough evidence to beat control under your chosen rules.
Low rates create fewer conversions per day. That means weaker evidence builds more slowly, so the model needs more time or more traffic.
No. A stronger prior only helps when it is credible and aligned with reality. Weak or biased prior assumptions can distort planning.
Yes. Use it when your success metric is a rate or binary outcome, such as click, conversion, accept, or win rate.
They prevent decisions based on very early noise. They also make the runtime estimate more realistic for production experiments.
No. This tool plans around posterior probability and priors. Frequentist tools plan around significance, alpha, and power targets.
Only if that stop rule was defined before launch. Predefined rules reduce bias and make your decision process more consistent.
Update the traffic realization factor and rerun the estimate. Lower traffic often lengthens runtime and may change planning decisions.