experimental-design-ds
A/B testing, randomization, sample size calculation, confounding control, and causal inference for data science. Covers the full experimental lifecycle from hypothesis formulation through power analysis, randomization strategies, blocking, factorial designs, sequential testing, and the potential outcomes framework for causal claims. Use when designing experiments, planning A/B tests, calculating sample sizes, or reasoning about causation from data.
npx skills add majiayu000/claude-skill-registry --skill experimental-design-ds --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Experimental Design for Data Science Experimental design is the discipline of collecting data so that the analysis can answer the intended question. Ronald Fisher, working at the Rothamsted agricultural station in the 1920s, formalized the three pillars -- randomization, replication, and blocking -- that remain the foundation of every modern experiment, from clinical trials to A/B tests on websites. This skill covers experimental design from the data scientist's perspective: planning experiments, ensuring valid causal inference, and avoiding the pitfalls that invalidate conclusions. **Agent affinity:** fisher (experimental design, ANOVA), tukey (exploratory analysis of experimental results), benjamin (ethical review of experiments) **Concept IDs:** data-hypothesis-testing, data-confidence-intervals, data-probability-basics, data-sampling-methods ## Fisher's Three Pillars ### 1. Randomization Random assignment of experimental units to treatment groups ensures that any observed difference is either due to the treatment or due to chance -- not due to confounders. Without randomization, the groups may differ systematically in ways that bias the result. **Mechanism:** Each unit is ass
- Fisher's Three Pillars
- 1. Randomization
- 2. Replication
- 3. Blocking
- The Experimental Lifecycle
- A/B Testing
- The Basic Framework
- Sample Size Calculation
- Common A/B Testing Pitfalls
- Randomization Strategies
- Factorial Designs
- Full Factorial
- Fractional Factorial
- Causal Inference
What does the experimental-design-ds skill do?
A/B testing, randomization, sample size calculation, confounding control, and causal inference for data science. Covers the full experimental lifecycle from hypothesis formulation through power analysis, randomization strategies, blocking, factorial designs, sequential testing, and the potential outcomes framework for causal claims. Use when designing experiments, planning A/B tests, calculating sample sizes, or reasoning about causation from data.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill experimental-design-ds --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.