Applied Statistics

Type I Error, Type II Error, and Power Analysis in R

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

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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