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Sequential Testing

What is Sequential Testing?

Traditional A/B testing best practices dictate that the readout of experiment metrics should occur only once, when the target sample size of the experiment has been reached. Continuous monitoring for the purpose of decision making results in inflated false positive rates (a.k.a. the peeking problem), much higher than expected based on the significance level selected for the test.

This is because p-values fluctuate and are likely to drop in and out of significance just by random chance, even when there is no real effect. Continuous monitoring introduces selection bias in the date we pick for the readout: Selectively choosing a date based on the observed results is essentially cherry-picking a stat-sig result that would never be observed if the data were to be analyzed only over the entire, pre-determined duration of the experiment. This increases the false positive rate (observing an experimental effect when there is none).

In Sequential Testing, the p-values for each preliminary analysis window are adjusted to compensate for the increased false positive rate associated with peeking. The goal is to enable early decision making when there's sufficient evidence while limiting the risk of false positives. While peeking is typically discouraged, regular monitoring of experiments with sequential testing is particularly valuable in some cases. For example:

  • Unexpected regressions: Sometimes experiments have bugs or unintended consequences that severely impact key metrics. Sequential testing helps identify these regressions early and distinguishes significant effects from random fluctuations.
  • Opportunity cost: This arises when a significant loss may be incurred by delaying the experiment decision, such as launching a new feature ahead of a major event or fixing a bug. If sequential testing shows an improvement in the key metrics, an early decision could be made. But use caution: An early stat-sig result for certain metrics doesn't guarantee sufficient power to detect regressions in other metrics. Limit this approach to cases where only a small number of metrics are relevant to the decision.

Quick Guide: Interpreting Sequential Testing Results

Click on Edit at the top of the metrics section in Pulse to toggle Sequential Testing on/off.


When enabled, an adjustment is automatically applied to results calculated before the target completion date of the experiment.


The dashed line represents the expanded confidence interval resulting from the adjustment. The solid bar is the standard confidence interval computed without any adjustments. If the adjusted confidence interval overlaps with zero, this means the metric delta is not stat-sig at the moment, and the experiment should continue its course as planned.

Sequential testing is a reliable way to make an early decision, particularly for early detection of regressions. One should be mindful that early decision-making will often result in underpowered lift estimates with a high degree of uncertainty. If making the right decision is important, you can use statistically-significant sequential testing results. If an accurate measurement is important, you should wait for full power as estimated by your pre-experimental power calculation. We do not calculate statistical power on post-hoc experimental results (See section "Post-hoc Power Calculations are Noisy and Misleading" in Kohavi, Deng, and Vermeer, A/B Testing Intuition Busters.

Statsig's Implementation of Sequential Testing

Statsig uses mSPRT based on the the approach proposed by Zhao et al. in this paper. The two-sided Sequential Testing confidence interval with significance level α\alpha is given by:


Here V is the variance of the difference in means, which can be obtained from the sample variance of the test and control group means:


τ\tau is the mixing parameter given by:


We have validated that this parameter satisfies the expected False Positive Rate and provides enough power to detect large effects early. More details on this analysis are available here.