Design and run A/B tests correctly
experimental-design-dsskillsetup L2★64
Tibsfox/gsd-skill-creator ↗What it does
A/B testing, randomization, sample size calculation, confounding control, and causal inference for data science
Best for
When designing experiments, planning a/b tests, calculating sample sizes, or reasoning about causation from data.
Outputs
- · formatted report
Requires
- · git
Preconditions
two groups (A = control, B = treatment), one intervention, one primary metric.
Failure modes
Invalid or missing input; Timeout or resource exhaustion