A/B tests & statistical inference
Check assignments, metrics, and samples against the design, then calculate effects and confidence intervals within the experiment's limits.
Inputs
Experiment protocol, assignment data, primary metric, sampling unit, and pre-specified analysis plan.
Deliverables
Assignment quality checks, effect and interval estimates, statistical results, and decision rationale.
Step-by-step workflow
Break the work into verifiable stages, then choose models and supporting tools.
- 01
Review the experiment design
Confirm randomization units, experiment duration, primary metrics, and pre-specified stopping conditions.
- 02
Check sample quality
Check allocation ratios, contamination, repeated users, missing values, and anomalies.
- 03
Run statistical calculations
Choose tests suited to the design and metric distribution. Calculate effect sizes and confidence intervals.
- 04
Explain business implications
Discuss uncertainty, practical benefits, and applicable populations, rather than significance alone.
Required model capabilities
Combine models for the actual stages. The directory includes candidates matching one or more of these capabilities.
View matching modelsSupporting tools
Selection & delivery checks
- Execute statistical code and disclose assumptions and parameters.
- Multiple comparisons, early stopping, and repeated inspection affect conclusions.
- Observational data cannot be interpreted as a randomized experiment.
An example request
Analyze an A/B test in Python, check allocation and data quality, calculate effect sizes and confidence intervals, and explain statistical and business implications.Find models for this request