A Primer on Statistical Power and Power Analysis

‘Power’ is a word that is often used, but not often understood. Here’s a quick primer on what it means…

If your experience is anything like mine, you’ve probably heard numerous people talk about ‘statistical power’ in conversations at work. I’m pretty sure that for the most part these people are pushing for larger sample sizes on the basis of some vague conception that a larger n is always good.

But how many of these people can actually define what statistical power is? In this article I want to take a look at the concept and definition of statistical power and identify where it is useful as a measure.

Hypothesis testing

The term ‘statistical power’ only has meaning when it is referring to a hypothesis test. You may recall that a hypothesis test involves using the statistical properties of a sample of data to determine the level of certainty of a statement about the overall population from which that sample was drawn. Let’s take an example. The salespeople data set from the peopleanalyticsdata R package contains the data of a sample of salespeople in a technology company, including their annual sales figures in thousands of dollars and their recent performance ratings on an increasing ordinal scale of 1 to 4. Let’s take a look at the first few rows.

library(peopleanalyticsdata)
salespeople <- salespeople[complete.cases(salespeople), ]…