Adaptive Experimentation Strategy

Use this skill to decide whether and how to use adaptive testing strategies instead of fixed-horizon A/B tests. It covers sequential testing, multi-armed bandits, Thompson sampling, contextual bandits, exploration/exploitation, and the data and engineering readiness required to operate them.

Source Traceability

Primary source: Next-Level A/B Testing by Leemay Nassery. Guidance is transformed and paraphrased from Chapter 7 on adaptive testing, sequential testing, multi-armed bandits, Thompson sampling, contextual bandits, and engineering requirements.

Related skills:

• experiment-type-selection for choosing simpler experiment types first.
• ml-experiment-evaluation for model and ranking evaluation paths.
• experiment-verification-monitoring for operational health and alerting.

Reference Routing

Need	Read
Adaptive testing concepts	references/core/knowledge.md
Readiness and strategy rules	references/core/rules.md
Scenario examples	references/core/examples.md
Step-by-step adaptive readiness plan	workflows/evaluate-adaptive-strategy.md

Workflow

1. State the decision and why fixed-horizon A/B testing may not be enough.
2. Decide whether the need is early stopping, reward maximization, or personalization.
3. Check data freshness, reward definition, dashboards, on-call ownership, and rollback controls.
4. Choose sequential testing, bandits, Thompson sampling, contextual bandits, or a simpler alternative.
5. Document exploration/exploitation tradeoffs and user/business risk.
6. Define rollout, monitoring, and adoption requirements.

Output Format

# Adaptive Experimentation Recommendation

## Use Case
[What decision or allocation problem motivates adaptive testing.]

## Recommended Strategy
[Do not use adaptive testing | Sequential | Bandit | Thompson sampling | Contextual bandit]

## Readiness
| Requirement | Status | Gap |
|-------------|--------|-----|

## Tradeoffs
- Reward:
- Exploration cost:
- Data freshness:
- Operational risk:

## Rollout Plan
1. [Step]
2. [Step]
3. [Step]

Quality Bar

• Do not recommend adaptive testing just because it is advanced.
• Do not use bandits when the real need is a clean causal estimate.
• Do not use contextual bandits without reliable context features and reward measurement.
• Do not ignore production requirements: stale data, bad allocation, and alert ownership can break adaptive systems.