experimental-design-sci
Experimental design principles for scientific inquiry. Covers variable identification and control, control groups (positive and negative), randomization, blinding, sample size and power, replication strategies, factorial designs, quasi-experimental approaches, and common design pitfalls. Use when designing, reviewing, or teaching about controlled experiments in any scientific discipline.
npx skills add majiayu000/claude-skill-registry --skill experimental-design-sci --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 Experimental design is the art and science of creating tests that can actually answer the question being asked. A well-designed experiment isolates the variable of interest, controls for confounds, produces data with enough precision and statistical power to discriminate between competing explanations, and does so within ethical and practical constraints. A poorly designed experiment wastes time, money, and trust. **Agent affinity:** mcclintock (experiment design), wu (measurement precision) **Concept IDs:** sci-controlled-experiments, sci-experimental-controls, sci-variables-types, sci-replication-reliability ## The Design Toolbox at a Glance | # | Design Element | Purpose | Key question | |---|---|---|---| | 1 | Variable identification | Clarify what changes and what is measured | What is the IV? What is the DV? What is held constant? | | 2 | Control groups | Provide a baseline for comparison | What does "no treatment" look like? | | 3 | Randomization | Eliminate systematic assignment bias | Are subjects assigned to conditions by chance? | | 4 | Blinding | Eliminate observer and subject bias | Do participants/observers know which condition they are in? | | 5
- The Design Toolbox at a Glance
- Element 1 -- Variable Identification
- Element 2 -- Control Groups
- Element 3 -- Randomization
- Element 4 -- Blinding
- Element 5 -- Sample Size and Statistical Power
- Element 6 -- Replication
- Element 7 -- Factorial Design
- Element 8 -- Quasi-Experimental Design
- Common Design Pitfalls
- Cross-References
- References
What does the experimental-design-sci skill do?
Experimental design principles for scientific inquiry. Covers variable identification and control, control groups (positive and negative), randomization, blinding, sample size and power, replication strategies, factorial designs, quasi-experimental approaches, and common design pitfalls. Use when designing, reviewing, or teaching about controlled experiments in any scientific discipline.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill experimental-design-sci --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.