experiment-designer

What This Skill Does

The experiment-designer skill is a specialized framework designed to inject statistical rigor into the product development lifecycle. It functions as an analytical partner that ensures product changes are backed by testable hypotheses rather than intuition. By standardizing the approach to experiment design, the skill assists in defining metrics, calculating necessary sample sizes, prioritizing backlog items through the ICE framework, and interpreting statistical outcomes with a focus on business significance rather than just p-values. It bridges the gap between raw data analysis and actionable product strategy.

Installation

To integrate this skill into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/alirezarezvani/experiment-designer Ensure you have the required dependencies for the script execution environment installed, specifically Python 3, as the included sample size calculator script requires it to run accurate power analyses.

Use Cases

• Hypothesis Validation: Rapidly draft if/then/because statements to align teams on the causal mechanism behind proposed features.
• Risk Mitigation: Use the skill to calculate exact sample sizes, preventing the common mistake of launching underpowered tests that result in inconclusive data.
• Backlog Prioritization: Utilize the ICE (Impact, Confidence, Ease) scoring system to make objective decisions about which experiments to run first in your roadmap.
• Result Analysis: Interpret complex A/B testing output to identify novelty effects, segment heterogeneity, and ensure that declared winners are statistically robust enough to scale.

Example Prompts

1. "I'm planning to test a new checkout button color. Help me write a robust hypothesis using the If/Then/Because format, and then determine the sample size needed for a 2% absolute uplift given a 15% baseline conversion rate."
2. "We have three experiments in our queue. Use the ICE framework to help me prioritize them based on the potential conversion impact, our current user confidence levels, and the engineering effort for each."
3. "Our latest A/B test shows a p-value of 0.04 with a 95% confidence interval that barely touches zero. How should I interpret these results, and should we roll out the feature?"

Tips & Limitations

• Fixed Horizons: Always define your test duration or sample size before the test begins. Avoid 'peeking' at results, as this inflates false positive rates (Type I errors).
• Guardrails: Always track secondary metrics. A win in primary conversion is invalid if it causes a significant drop in customer support tickets or site performance.
• Business vs. Stat Significance: A statistically significant result isn't always useful. If the effect size is smaller than the cost to maintain the feature, prioritize other work.
• Limitations: This skill is an analytical aid. It assumes your instrumentation and data collection are reliable. Always perform a sanity check for sample ratio mismatch before interpreting test outcomes.