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Ab Testing Setup

Design and implement A/B test for model comparison including experimental design, traffic splitting, statistical power analysis, and significance testing.

Complexity: Complex | Duration: 30m+ | Category: Devops

Tags: ml ab-testing experimentation statistics

Workflow Diagram​

Steps​

Step 1: Hypothesis definition​

Agent: data

scientist - Define null/alternative hypotheses

Step 2: Power analysis​

Agent: data

scientist - Calculate required sample size for significance

Step 3: Experimental design​

Agent: ml

engineer - Design randomization and stratification strategy

Step 4: Traffic splitting​

Agent: ml

engineer - Implement 50/50 or weighted split with consistent hashing

Step 5: Metric instrumentation​

Agent: ml

engineer - Track primary and secondary metrics

Step 6: Guardrail setup​

Agent: testing

specialist - Define acceptable metric boundaries

Step 7: Statistical testing​

Agent: data

scientist - Run t-test, chi-square, or Mann-Whitney U

Step 8: Results analysis​

Agent: data

scientist - Interpret p-values, effect sizes, confidence intervals

Usage​

To execute this workflow:

/workflow devops/ab-testing-setup.workflow

See other workflows in this category for related automation patterns.