After recently leading my team through the complex process of evaluating AB testing and digital experimentation tools (Adobe Target, VWO, Optimizely, Statsig, Eppo, and others), I've realized that many internal stakeholders don't fully understand what's happening under the hood of these platforms beyond what's presented during product demos.

The statistical engine powering your experiments isn't just a technical detail—it fundamentally changes how fast you can make decisions, the reliability of those decisions, and the trade-offs you accept. What surprised me most during our evaluation wasn't the feature differences, but how dramatically different the statistical approaches impacted both accuracy and speed.

The Frequentist Foundation: Adobe Target's Traditional Approach

Adobe Target represents a rigorous implementation of traditional frequentist statistics. Their approach uses two-tailed Welch's t-tests with 95% confidence intervals, specifically choosing this conservative approach to maintain statistical validity.

Target uses Welch's t-test instead of Student's t-test because it properly handles unequal variances between test groups—a common real-world scenario. Their sample size calculator incorporates Bonferroni corrections for multiple comparisons, though you must manually apply them.

The challenge with Target is the peeking problem. Adobe explicitly warns that checking results before reaching your calculated sample size increases false positive rates significantly. This locks you into predetermined test durations regardless of early trends.

The frequentist approach excels when you have high-traffic sites and can afford to wait for statistical certainty. Target's methodology produces results that regulatory bodies trust and auditors approve—crucial for financial services, healthcare, and other regulated industries.

VWO's SmartStats: Bayesian Implementation

VWO's SmartStats engine represents a comprehensive commercial implementation of Bayesian A/B testing. After transitioning from frequentist methods in 2016, VWO built an engine that answers business questions more directly: "What's the probability this variant is better?"

VWO uses Beta-Binomial conjugate pairs for conversion rate testing and implements Monte Carlo sampling for complex revenue models. Their loss-based decision framework allows you to set business-relevant thresholds rather than arbitrary significance levels.

However, like most "Bayesian" A/B testing tools, VWO's implementation uses what are called "non-informative priors." Research suggests these priors aren't truly non-informative and can influence results in ways that aren't always transparent¹. The advantage is more intuitive communication with stakeholders; the trade-off is less statistical transparency than pure frequentist approaches.

Google Optimize's Quiet Exit

Google Optimize's sunset in September 2023 reveals important industry tensions. Optimize used Bayesian inference generating probability-to-beat-baseline metrics that business users understood intuitively.

But Optimize suffered from a "black box" problem. Google provided minimal documentation about their mathematical implementation, making it difficult for statisticians to validate results. When they transitioned experimentation to Firebase A/B Testing, they chose frequentist inference—a shift suggesting challenges in scaling Bayesian methods with appropriate rigor.

The lesson: transparency matters as much as methodology. Trust requires understanding how the system works.

Optimizely's Stats Engine: Innovations and Trade-offs

Optimizely's Stats Engine uses mixture Sequential Probability Ratio Tests (mSPRT) developed with Stanford researchers. They've attempted to solve the peeking problem through "always valid inference" while maintaining frequentist foundations.

The Innovation: Optimizely uses False Discovery Rate (FDR) control instead of traditional Type I error control, which better matches business decision-making patterns. Their always-valid p-values allow continuous monitoring without the traditional peeking penalty.

The Trade-offs: Recent research has identified several limitations:

• Optimality conditions don't apply to ratio metrics like conversion rates—the most common A/B test metric²

• Statistical power is significantly lower than alternative sequential methods (approximately 62% vs 80% for group sequential approaches)³

• The system uses a prior calibrated from historical experiments across Optimizely's entire platform, pooling data from unrelated industries⁴

Additionally, the likelihood ratio calculations assume a matching design that's difficult to verify in practice, and independence assumptions may be violated by repeated user interactions².

When It Works: Optimizely's approach can be valuable when detecting very large effects (>7.5% improvement) where the speed advantages materialize and power penalties matter less. For regulatory compliance or smaller effect sizes typical in mature optimization programs (2-5%), the power trade-offs become more significant.

LaunchDarkly's Flexible Approach

LaunchDarkly offers both Bayesian and frequentist options within the same platform, eliminating friction when statisticians prefer one approach while business stakeholders understand another better.

Their Bayesian implementation uses empirical priors with automatic outlier mitigation. Their frequentist approach includes Bonferroni corrections and sample size planning tools. Their CUPED implementation (Controlled-experiment Using Pre-Experiment Data) can reduce variance by 14-86% depending on historical correlation, potentially reducing test duration significantly.

The Modern Platform Landscape

Newer platforms like Statsig, Eppo, and Amplitude Experiment are standardizing around sequential testing while introducing innovations that address traditional limitations.

Statsig: Transparency and Usability

Statsig uses frequentist methods with two-sided unpooled z-tests, but emphasizes transparency. They provide complete SQL query access and publish detailed explanations of their statistical methods.

Statsig's sequential analysis capabilities allow for statistically valid results at any monitoring point. This addresses the peeking problem while maintaining the rigor that statisticians require. Their emphasis on proper randomization and targeting helps reduce bias.

Eppo: Warehouse-Native Sophistication

Eppo builds their statistical engine directly on your data warehouse, enabling statistical methods that weren't possible with traditional platforms. They support frequentist, sequential, and Bayesian analysis with complete transparency.

Eppo's CUPED++ implementation runs full linear regression on all metrics plus assignment properties, potentially reducing variance by 20-65%. They provide proper Benjamini-Hochberg false discovery rate control and offer contextual bandits for AI-powered optimization.

Their Bayesian implementation uses empirical priors with natural shrinkage, protecting against inflated effect sizes in underpowered tests—directly reducing false positives.

Understanding Type I and Type II Errors

Different platforms handle statistical errors in ways that directly impact business outcomes. Type I errors (false positives) cost money by implementing ineffective changes. Type II errors (false negatives) cost opportunity by missing genuine improvements.

Traditional frequentist platforms like Adobe Target control Type I error rates precisely—guaranteeing exactly 5% false positives at 95% confidence. This rigidity means you cannot adapt error rates to business context.

Bayesian platforms like VWO handle error rates more flexibly through loss-based frameworks. Instead of arbitrary significance thresholds, you can set business-relevant loss tolerances.

Newer platforms like Eppo and Statsig address this through different mechanisms. Eppo's Bayesian implementation uses natural shrinkage to protect against inflated effect sizes. Statsig's sequential analysis provides always-valid inference while maintaining transparency about the statistical methods used.

Why Bayesian Statistics Resonates with Stakeholders

Bayesian results are often easier to communicate: "there's an 85% chance the new design is better" versus explaining frequentist confidence intervals correctly. Research shows that even statistics professors frequently misinterpret frequentist statistics⁵.

However, this communication advantage comes with implementation challenges. Most commercial "Bayesian" tools use priors that aren't truly non-informative, and the mathematical foundations can be just as opaque as frequentist approaches when not properly documented¹.

The credible intervals Bayesian methods provide align with natural business thinking, but only when the underlying statistical implementation is sound and transparent.

Why Newer Tools Are Gaining Ground

Having led our team through comprehensive platform evaluation, several factors explain why newer tools are winning deals:

Transparency: Traditional platforms often operate as "black boxes." Newer tools provide SQL access to calculations and publish detailed technical documentation, building trust and enabling validation.

Warehouse-Native Architecture: Modern companies store data in cloud warehouses. Warehouse-native approaches eliminate data movement costs, maintain governance standards, and avoid vendor lock-in.

Modern Statistical Methods: While established platforms retrofit advanced statistics onto legacy architectures, newer tools build them in from day one with more sophisticated implementations.

Developer Experience: Newer platforms integrate with CI/CD pipelines, provide programmatic APIs, and treat experimentation as part of the development process rather than a separate activity.

Choosing the Right Approach

Context matters more than theoretical statistical superiority. Consider:

Choose Frequentist When:

• High-stakes, irreversible decisions

• Regulatory compliance requirements

• Abundant traffic making sample size constraints irrelevant

• Team needs reproducible, validated statistical methods

Choose Bayesian When:

• Iterative improvement cycles

• Limited sample sizes

• Relevant prior knowledge exists

• Fast decision-making is prioritized

• Non-technical stakeholders need intuitive communication

Choose Sequential (Group Sequential or mSPRT) When:

• Need to balance speed with statistical rigor

• Want protection against peeking problems

• Can accept some power trade-offs for flexibility

• Have statistical expertise to understand trade-offs

Critical Evaluation Framework

When evaluating platforms, ask:

1. What is the actual statistical power for your typical effect sizes? Don't accept marketing claims—request simulation data.

2. Can you reproduce the statistical calculations? Platforms should provide enough documentation for your data science team to validate results.

3. What are the specific trade-offs? Every statistical approach involves trade-offs between speed, power, and flexibility. Understand what you're accepting.

4. How does early stopping affect bias? If you plan to stop tests early on positive results, understand the bias this introduces in effect size estimates².

5. What happens with multiple metrics? How does the platform handle multiple comparisons, and does this match your business needs?

The Future of Experimentation Statistics

The industry is moving toward more sophisticated statistical methods that don't sacrifice usability. Sequential testing is becoming standard, variance reduction techniques like CUPED are being integrated by default, and platforms are making advanced statistics accessible to non-technical users.

I expect we'll see more hybrid approaches and continued emphasis on transparency. The computational challenges that once limited statistical sophistication are disappearing as cloud computing becomes cheaper.

What's most important is that statistical advances are democratizing sophisticated experimentation. Small teams can now access methods previously limited to tech giants with dedicated statistics teams.

Conclusion

The statistical engine powering your A/B tests is a strategic choice affecting optimization velocity, stakeholder communication, and competitive advantage. However, it's crucial to understand that every approach involves trade-offs.

During our evaluation, we discovered that no platform is universally superior. The right choice depends on your traffic volume, typical effect sizes, organizational sophistication, regulatory requirements, and risk tolerance.

Most importantly, we learned that transparency and understanding matter as much as the statistical methodology itself. Choose platforms where you can validate the statistics, understand the trade-offs, and trust the results for your specific business context.

References

1. Georgiev, G. (2017). "5 Reasons to Go Bayesian in AB Testing – Debunked." Analytics Toolkit. https://blog.analytics-toolkit.com/2017/5-reasons-bayesian-ab-testing-debunked/

2. Larsen, N., Stallrich, J., Sengupta, S., Deng, A., Kohavi, R., & Stevens, N.T. (2024). "Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology." The American Statistician, 78(2), 135-149. https://doi.org/10.1080/00031305.2023.2257237

3. Georgiev, G. (2022). "Comparison of the statistical power of sequential tests: SPRT, AGILE, and Always Valid Inference." Analytics Toolkit. https://blog.analytics-toolkit.com/2022/power-and-average-sample-size-of-sequential-tests/

4. Le, P. (2022). "A critique of Optimizely." https://blog.patrick-le.com/2022/12/07/a-critique-of-optimizely/

5. Schultzberg, M. & Ankargren, S. (2023). "Choosing a Sequential Testing Framework — Comparisons and Discussions." Spotify Engineering. https://engineering.atspotify.com/2023/03/choosing-sequential-testing-framework-comparisons-and-discussions/