Happy Halloween

Power Analysis allows you to ask:

How big of a sample do I need to detect a given effect size with a given level of confidence.

Steps in a power analysis

  • decide on parameters for effect size, error magnitude
  • decide how much power you want
  • write a function to simulate data with your effect size, error magnitude, sample size
  • write a for loop to increase sample size until desired power is achieved

Type of errors - Redux

The Truth Retain \(H_0\) Reject \(H_0\)
\(H_0\) True Correct! Type I error (\(\alpha\))
\(H_0\) False Type II error (\(\beta\)) Correct!

In power analysis, \(H_0\) is false, so any non-significant p values are Type II errors.

In a given bunch of simulations, power = \(1 - \beta\)

A common target is a power of 0.8

Remember, this is a balancing act! The more powerful, the more chance of commiting Type I error, because Type I and Type II are inversely related.

You already wrote a function for ANOVA power analysis, in your homework!

doANOVA <- function(sampleSize=20, grandMean=50, errorSD=5, meanDiff=3, showPlot=TRUE) {
  myError <- rnorm(sampleSize, mean = 0, sd = errorSD)
  groups <- rep(c("groupX", "groupY"), sampleSize/2)
  y <- grandMean + c((meanDiff/2)*-1, (meanDiff/2)) + myError
  if(showPlot == TRUE) print(qplot(x=groups, y=y, geom="boxplot", fill=groups))
  myModel <- lm(y~groups)
            lower.tail = FALSE))

We are set up to do a power analysis

doANOVA(sampleSize=20, errorSD=5, meanDiff=3)

##     value 
## 0.1553212

Calculate power for a given sample size

To figure out \(beta\) (Type II error rate) for a given sample size, we need to run our function a bunch of times

calculatePower <- function(n){
  for(i in 1:500){
    results[i] <- doANOVA(sampleSize=n, errorSD=5, meanDiff=3, showPlot=FALSE)
  beta <- sum(results>0.05) / length(results)
  return(1 - beta)
## [1] 0.252
At this sample size, we aren't very consistently detecting this mean difference between groups.

Let's figure out how large our sample size needs

We want to detect our effect at least 80% of the time

So, we can start with our sample size of 20, which we know is too low, and increment our sample size up and up until we hit a power of 0.8

Another loop to find n with power >= 0.8

currentPower <- 0
sampleSize <- 20

while(currentPower < 0.8){ 
  currentPower <- calculatePower(n=sampleSize)
  sampleSize <- sampleSize + 4
## [1] 0.814
## [1] 96