Decoding Statistical Power: The Key to Precision in Marketing Studies
Last Updated on November 11, 2023 by Editorial Team
Author(s): Deepak Chopra | Talking Data Science
Originally published on Towards AI.
Marketing Measurement-4: A layman’s guide to Statistical Power and its interplay with other factors while measuring marketing effectiveness.
Top highlight
Photo by Thomas Kelley on Unsplash
This article is a continuation of the series on ‘Measuring Marketing Effectiveness’, in case you missed the previous parts, here is a quick recap:
Part 1 highlighted the superiority of experimental design, AB testing, or test-control analysis as the best-in-class methodology for measuring marketing interventions.Part 2 double-clicked into “Marketing Incrementally” via the Test-Control framework and emphasized the importance of formulating hypotheses (both Null and Alternate) in Part 2.Part 3 touched on why “significance” is important for marketing incrementality, what it represents, and how to get to it.
Null Hypothesis (H0) is defined as the hypothesis of… Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.
Related posts
Recent Posts
