Experiment Designer Skill

Produce rigorous experiment designs from product hypotheses, and interpret results with statistical and practical significance — so you can defend every decision to a sceptical engineering lead or data scientist.

Required Inputs

Work from what is given. If any of these is missing, still produce the complete output: fill each gap with a reasonable assumption marked (assumed, confirm) and list those assumptions at the end. Ask a question first only when the core subject itself is missing (there is nothing to write about). For experiment design:

• Hypothesis (what change, what metric, what expected movement)
• Current baseline metric value
• Minimum detectable effect (MDE) — the smallest lift worth caring about
• Available daily sample size

For results interpretation:

• Control and variant results (raw numbers or percentages)
• P-value or confidence interval
• Run duration (days)
• Any anomalies observed during the test