1. Psychology 202a Advanced Psychological Statistics December 1, 2015

2. The plan for today Continuing discussion of power Power for t tests Power for ANOVA G*power

3. Consider that in tabular form: H 0 TrueH 0 False H 0 Rejected Type I error (p =  Great! H 0 RetainedNo problem Type II error (p = 

4. What is power? In that scenario, power = 1 –  In other words, power is the probability that we will avoid a Type II error, given that the null hypothesis is actually false.

5. What affects power? To understand what affects power, consider the simplest possible situation for testing a hypothesis about means: –Hypothesis about a single mean; –The population standard deviation is known to be 15; –The sample size is 25; –The null hypothesis is that  = 100. –The truth is that  = 105.

6. When will we reject the null? We’ll be doing a Z test. We’ll reject the null if Z 1.96.

8. But the null hypothesis isn’t true! We stipulated that  is really 105. In that case, the expected value of the Z statistic is really (105-100)/3 = 5/3, not 0.

10. What is the power? What is the probability that a single draw from a normal distribution with mean 5/3 and standard deviation 1 will be 1.96? pnorm(-1.96,5/3,1) + (1-pnorm(1.96,5/3,1)) So the power is about 0.38.

11. What affects power? Power will be increased by: –anything that tends to make the test statistic large; –anything that tends to make the critical value small.

12. Things that make the statistic large: Big effect Small variability Big sample size

13. Things that make the critical value small: Less stringent alpha level One-tailed tests In most cases, bigger sample size (because for more complicated statistics, the critical value depends on degrees of freedom)

14. But when will we ever do a Z test? Noncentral distributions. Noncentrality parameter expresses exactly how the null hypothesis is false (with a bit of sample size thrown in). delta <- (105-100)/3 tcrit <- qt(.975,24) pt(-tcrit,24,delta) + (1-pt(tcrit,24,delta)) So power would really be a bit lower:.36 rather than.38.

15. Power in more complex testing situations Two-sample t test Calculation in R: delta <- (105-100)/15 * sqrt(12*13/25) tcrit <- qt(.975,23) pt(-tcrit,23,delta) + (1-pt(tcrit,23,delta))

16. Using G*power Obtaining and installing Power calculations in G*power –power for a given situation –required n for a given power –minimum detectable effect size

17. Power analysis for ANOVA If your main interest is in a contrast, do the power analysis for that contrast (as if it were a t test using MS e in place of the pooled variance estimate. Power analysis for the omnibus F test:

18. ANOVA power example Suppose we are planning an experiment with five groups, and we expect the means to be spread over a ± one standard deviation range. Pick an arbitrary standard deviation (say, 10). So the means might be (40, 45, 50, 55, 60).

19. ANOVA power example So how large does n need to be to give power of, say,.9?

20. ANOVA power example In R: n <- seq(2,15,1) dfn <- 4 dfd <- 5*(n-1) fcrit <- qf(.95, dfn, dfd) lambda <- 2.5*n power <- 1-pf(fcrit,dfn,dfd,lambda) cbind(n,power)

21. ANOVA power example Illustration in G*power

22. Next time Two-way ANOVA