Published by

Nicole Radziwill

Image Credit: Doug Buckley of http://hyperactive.to

At some point in the life of most quality engineers, quality managers, and Six Sigma Black Belts and practitioners — you will have to compute an appropriate sample size to ensure that the results from your study will be statistically significant. To achieve this, you might have to perform a power analysis (which I know sounds really scary, but it doesn’t have to be).

Here’s the powerpoint and R exercise that I use in my classes to compute sample sizes, perform power analysis, and plot power curves to help me choose the most appropriate sample sizes. Feel free to use, reproduce, recreate, etc. — just please use appropriate attributions and let me know if this is helpful to you!

Nicole

Radziwill-type-i-ii-power-effect (Powerpoint Discussion of Type I/II/Power)

power-sample-size-75-925 (R Exercise to Compute Sample Sizes and Plot Power Curves)

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One response to “Type I Error, Type II Error, and Power Analysis in R”

Quality and Innovation

February 9, 2015

[…] I, Type II, and Power Analysis – [Book Chapter + PPT] – If you’re planning a statistical inference test, it’s important to know how […]

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I’m Nicole

Since 2008, I’ve been sharing insights and expertise on Digital Transformation & Data Science for Performance Excellence here. As a CxO, I’ve helped orgs build empowered teams, robust programs, and elegant strategies bridging data, analytics, and artificial intelligence (AI)/machine learning (ML)… while building models in R and Python on the side. In 2025, I help leaders drive Quality-Driven Data & AI Strategies and navigate the complex market of data/AI vendors & professional services. Need help sifting through it all? Reach out to inquire – check out my new book that reveal the one thing EVERY organization has been neglecting – Data, Strategy, Culture & Power.

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