A/B vs Multi-armed bandit experiments mab-vs-ab
This page provides a detailed comparison of A/B and Multi-Armed Bandit experiments, explaining their respective strengths, limitations, and the scenarios in which each approach is most effective.
A/B ab-test
Traditional A/B experiment involves splitting traffic equally across treatments and maintaining this allocation until the experiment concludes. Once statistical significance is reached, the winning treatment is identified and subsequently scaled.
Advantages
The key strengths of traditional A/B experiment are:
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Statistical Rigor
The fixed design provides well-defined error rates and confidence intervals.
Hypothesis testing frameworks, e.g. 95% confidence, are easier to apply and interpret.
Properly powered experiments reduce the likelihood of false positives.
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Simplicity
The methodology is straightforward to design and execute.
Results can be communicated clearly to non-technical stakeholders.
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Comprehensive Data Collection
Each treatment receives adequate exposure, enabling analysis not only of the winning variant but also of underperforming alternatives.
This additional information can inform long-term strategic decisions.
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Bias Control
Fixed allocation reduces susceptibility to biases such as the “winner’s curse” or regression to the mean.
Limitations
The main limitations of traditional A/B experiment are:
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Opportunity Cost
A substantial proportion of traffic is directed toward inferior treatments, potentially reducing conversions or revenue during the test.
The winning treatment cannot be implemented until the experiment concludes.
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Fixed Duration Requirement
Tests must generally run for their pre-specified horizon, even if external conditions, e.g. seasonality, market shifts, change mid-way.
Adaptation during the experiment is limited.
Multi-armed bandit mab-experiment
Multi-armed bandit algorithms use adaptive allocation: as evidence accumulates, more traffic is directed toward better-performing treatments. The objective is to maximize cumulative reward during the experiment rather than focus solely on the final result.
Advantages
The key strengths of Multi-armed bandit methods are:
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Faster Optimization
Promising treatments are prioritized earlier, improving overall performance during the test.
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Adaptivity
Allocations update continuously as data is collected, making Multi-armed bandit suitable for dynamic environments.
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Reduced Opportunity Cost
Poor treatments are phased out quickly, minimizing wasted traffic.
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Suitability for Continuous Testing
Effective for ongoing experimentation or contexts where traffic is costly.
Limitations
The main limitations of Multi-armed bandit methods are:
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Weaker Statistical Guarantees
Traditional hypothesis testing is harder to apply, and stopping rules are less clear.
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Reduced Transparency
Adaptive allocation can be difficult to explain to stakeholders.
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Limited Information on Underperforming Treatments
Weak treatments receive little exposure, limiting diagnostic insight.
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Implementation Complexity
Requires advanced algorithms and infrastructure, with greater potential for misconfiguration.