The Case for Group Sequential Testing in A/B Testing
Published on Oct 27, 2025
by Zoe Oakes
How modern experimentation teams cut decision time by 20–80%, without cutting corners.
If your experimentation program still waits until the very end of a test to make decisions, you’re not capitalizing on speed, and therefore value. Group Sequential Testing (GST) gives you a disciplined way to look early, stop early, and still keep rigorous error control.
With ABsmartly, GST is no longer an academic idea. It’s a production-ready feature that helps teams ship winners and kill losers weeks faster.
What is Group Sequential Testing?
GST schedules a small number of planned interim looks at your data (for example, after 25%, 50%, and 75% of your planned sample). At each look, the system checks whether your results have crossed adjusted efficacy (positive) or futility (negative) thresholds.
Efficacy stop: Your experiment is working well enough; you can stop and ship early.
Futility stop: There’s little chance of detecting an effect; you can stop and move on.
ABsmartly automatically handles the statistical adjustments that keep your false positive rate in check, even with multiple looks.
Why GST Matters
Compared with “wait until the end” fixed-horizon testing, GST can:
Shorten test durations by 20–80% on average, depending on effect sizes and boundaries
Protect users by cutting off underperforming or harmful variants earlier
Free up capacity to run more experiments in the same calendar time
Fully sequential testing involves continuously monitoring incoming data and allows an experiment to stop at any moment once statistical criteria are met. While it offers maximum flexibility and speed in decision-making, it requires strict statistical controls to avoid inflating false positive rates. In contrast, ABsmartly’s group sequential testing is a more practical approach where data is reviewed at predefined checkpoints, such as after 25%, 50%, or 75% of the data is collected. This method balances flexibility with statistical rigor, allowing early stopping while reducing the risk of false positives compared to fully sequential testing.
For a fuller explanation of GST and why it matters, you can read Georgi Georgiev’s whitepaper on Group Sequential Methods in Online A/B Testing.
Feature | Fully Sequential Testing (FST) | ABsmartly’s Group Sequential Testing (GST) |
When you can look | Anytime, continuously | Planned checkpoints (flexible timing allowed) |
Ease of explanation | Easy if boundaries are shown | Easy |
False positive guarantees | Weaker, depends on exact stopping rules | Strong, close to fixed-horizon |
Statistical power | Lower for same sample size | Much higher, close to fixed-horizon |
Speed of decisions | Can stop at any moment, but may take longer to detect small effects | Faster and more reliable decisions |
Operational risk | High, very tempting to stop too early | Lower, natural “decision gates” |
Best fit for | Very high-frequency tests, niche real-time use cases, but perfect for secondary metrics | Most product experiments and feature rollouts |
Why ABsmartly’s GST is Practitioner-Friendly
20–80% faster tests with frequentist guarantees. GST is implemented with alpha-spending methods proven in clinical trials, adapted for product teams.
Stack-wide experimentation. Run tests across front-end, back-end, mobile, and even CRM/offline integrations—all with the same statistical rigor.
Results built for clarity. The ABsmartly UI differentiates fixed-horizon vs. GST metrics so PMs and analysts don’t misread results.
Data control options. Deploy ABsmartly in your own private cloud or on-prem setup, essential for regulated industries.
Jonas Alves’s Vision
ABsmartly’s GST engine didn’t appear out of nowhere. Founder Jonas Alves has spent over a decade scaling experimentation programs: training teams, adding guardrails, and pushing experimentation beyond web pages into every part of the product. His vision has always been simple:
“Our goal is to make experimentation faster, safer, and accessible to every team, not just data scientists.”
GST inside ABsmartly is a direct reflection of that vision. ABsmartly’s group sequential testing (GST) is a sophisticated statistical method that goes well beyond traditional group sequential designs:
Interim analyses with valid stopping boundaries (“checkpoints”)
ABmartly supports early checks to enable early stopping without inflating erro rates.
Flexible “futility type” choice: binding vs non‑binding futility
Futility type defines what happens when the experiment crosses the futility boundary. A non‑binding futility type, on the other hand, means that the experiment will continue even after it has crossed a futility boundary.
3. Error control / confidence and power configuration
Our platform lets the experimenter select confidence (i.e. false positive tolerance) and power.
Flexible checkpoint spacing / minimal intervals
ABsmartly allows you to define a minimal interval between interim analyses (so you cannot run them arbitrarily quickly).
All these features make ABsmartly’s GST method significantly more advanced and flexible than standard group sequential testing designs
Takeaway
Group Sequential Testing gives experimenters the best of both worlds:
Rigorous statistics that hold up under scrutiny
Practical speedups that free you to learn faster
With ABsmartly’s implementation, you can stop waiting until the bitter end of every test. Instead, you can make faster, safer, and smarter product decisions.
Ready to see how GST can cut weeks off your experimentation cycle?
Book a demo with ABsmartly.