MAHMOUD ABDELNABY
Cro · TEMPLATE FRAMEWORK · 1

CRO Experiment Prioritization & Statistical Power Scorecard

Prioritize high-impact A/B tests and calculate required sample sizes and test durations before writing a single line of code.

Problem it addresses

Develop a professional CRO experiment prioritization system: 1) Extended ICE/PIE scoring framework incorporating Traffic Feasibility and Technical Effort, 2) Python sample size calculator (calc_power.py) computing required sample sizes and test duration (days) given baseline conversion rate and desired MDE, 3) Complete Markdown experiment planning template.

Who it is for

PRO operating in high-growth e-commerce, digital publishing, and direct-response marketing across Saudi Arabia, UAE, and Egypt.

What is inside

Key files listed in the README repository architecture:

  • examples/ecommerce-experiment-roadmap.md
  • examples/sample_cro_backlog.csv
  • framework/prioritization-rubric.md
  • LICENSE
  • models/cro-experiment-backlog.xlsx
  • playbooks/sample-size-calculator.py
  • README.md
  • src/calc_power.py
  • templates/experiment-design-document.md
  • templates/ice-pie-scoring-model.md
  • tests/test_calculator.py
  • tests/test_power.py

How to evaluate and use it

  1. Read the README first and confirm that the documented problem matches your use case.
  2. Inspect the listed files to find the data, tools, templates, and tests relevant to your task.
  3. Follow the repository’s Quick Start and check prerequisites before running commands.
  4. Compare outputs with documented examples and review limitations before using them in a decision or client workflow.

Quick Start from the README: python -m unittest discover -s tests · python src/calc_power.py --help

Scope and limitations

  • Statistical Power Requirements: Enforces minimum sample size calculation before launching split tests, preventing tests with insufficient statistical power.
  • ICE vs P.I.E. Hybrid Scoring: Combines Potential, Importance, and Ease with regional implementation risk multipliers.
  • Guardrail Metric Tracking: Prioritization models require explicit guardrail metrics (e.g. Return Rate, Unsubscribe Rate) to prevent destructive short-term conversion gains.

This page summarizes the available documentation; it does not establish performance or business results. Verify data and prerequisites before use.

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