Designing Content Experiments
Quick start
Collect or infer:
- •Hypothesis to test
- •Success metric and current baseline
- •Audience and traffic volume available
- •Technical constraints (what can be tested)
- •Timeline and decision criteria
Then produce output using TEMPLATES.md. Validate with RUBRIC.md.
Workflow
- •Define clear, falsifiable hypothesis
- •Identify primary metric and guardrail metrics
- •Calculate required sample size for statistical significance
- •Design control and variant(s)
- •Document targeting and traffic allocation
- •Set duration and stopping criteria
- •Define decision framework (what actions follow which outcomes)
- •Run the rubric check. Revise until it passes.
Degrees of freedom
Freedom level: Low
- •Default: follow templates exactly
- •Allowed variation: number of variants (recommend 2-3 max), specific metrics tested—as long as rubric passes
- •Strict constraints: Must include sample size calculation; must define stopping criteria; must have decision framework
State awareness
- •First experiment: Start simple (A/B, single variable); focus on learning process
- •Mature program: Can run multivariate tests; consider interaction effects
- •Low traffic: Longer duration or sequential testing; consider Bayesian approach
- •High stakes: Require higher confidence level (99% vs 95%)
References
- •Templates: TEMPLATES.md
- •Rubric: RUBRIC.md
- •Examples: EXAMPLES.md