Statistical Inference: Power

Packages

library(tidyverse)

Errors in testing

What can happen:

Decision
Truth Do not reject Reject null
Null true Correct Type I error
Null false Type II error Correct

Tension between truth and decision about truth (imperfect).

  • Prob. of type I error denoted \(\alpha\). Usually fix \(\alpha\), eg. \(\alpha = 0.05\).
  • Prob. of type II error denoted \(\beta\). Determined by the planned experiment. Low \(\beta\) good.
  • Prob. of not making type II error called power (= \(1 - \beta\)). High power good.

Power

  • Suppose \(H_0 : \mu = 10\), \(H_a : \mu \ne 10\) for some population mean \(\mu\).
  • Suppose \(H_0\) wrong. What does that say about \(\mu\)?
  • Not much. Could have \(\mu = 11\) or \(\mu = 8\) or \(\mu = 496\). In each case, \(H_0\) wrong.
  • How likely a type II error is depends on what \(\mu\) is:
    • If \(\mu = 496\), should be able to reject \(H_0 : \mu = 10\) even for small sample, so \(\beta\) should be small (power large).
    • If \(\mu = 11\), might have hard time rejecting \(H_0\) even with large sample, so \(\beta\) would be larger (power smaller).
  • Power depends on true parameter value, and on sample size.
  • So we play “what if”: “if \(\mu\) were 11 (or 8 or 496), what would power be?”.

Figuring out power

  • Time to figure out power is before you collect any data, as part of planning process.
  • Need to have idea of what kind of departure from null hypothesis of interest to you, eg. average improvement of 5 points on reading test scores. (Subject-matter decision, not statistical one.)
  • Then, either:
    • “I have this big a sample and this big a departure I want to detect. What is my power for detecting it?”
    • “I want to detect this big a departure with this much power. How big a sample size do I need?”

How to understand/estimate power?

  • Suppose we test \(H_0 : \mu = 10\) against \(H_a : \mu \ne 10\), where \(\mu\) is population mean.
  • Suppose in actual fact, \(\mu = 8\), so \(H_0\) is wrong. We want to reject it. How likely is that to happen?
  • Need population SD (take \(\sigma = 4\)) and sample size (take \(n = 15\)). In practice, get \(\sigma\) from pilot/previous study, and take the \(n\) we plan to use.
  • Idea: draw a random sample from the true distribution, test whether its mean is 10 or not.
  • Repeat previous step “many” times.
  • “Simulation”.

Making it go

  • Random sample of 15 normal observations with mean 8 and SD 4:
x <- rnorm(15, 8, 4)
x
 [1] 14.487469  5.014611  6.924277  5.201860  8.852952
 [6] 10.835874  3.686684 11.165242  8.016188 12.383518
[11]  1.378099  3.172503 13.074996 11.353573  5.015575
  • Test whether x from population with mean 10 or not (over):

…continued

t.test(x, mu = 10)

    One Sample t-test

data:  x
t = -1.8767, df = 14, p-value = 0.08157
alternative hypothesis: true mean is not equal to 10
95 percent confidence interval:
  5.794735 10.280387
sample estimates:
mean of x 
 8.037561
  • Fail to reject the mean being 10 (a Type II error).

or get just P-value

ans <- t.test(x, mu = 10)
ans$p.value
[1] 0.0815652

How I knew it was called that

str(ans)
List of 10
 $ statistic  : Named num -1.88
  ..- attr(*, "names")= chr "t"
 $ parameter  : Named num 14
  ..- attr(*, "names")= chr "df"
 $ p.value    : num 0.0816
 $ conf.int   : num [1:2] 5.79 10.28
  ..- attr(*, "conf.level")= num 0.95
 $ estimate   : Named num 8.04
  ..- attr(*, "names")= chr "mean of x"
 $ null.value : Named num 10
  ..- attr(*, "names")= chr "mean"
 $ stderr     : num 1.05
 $ alternative: chr "two.sided"
 $ method     : chr "One Sample t-test"
 $ data.name  : chr "x"
 - attr(*, "class")= chr "htest"

Run this lots of times

  • without a loop!
  • use rowwise to work one random sample at a time
  • draw random samples from the truth
  • test that \(\mu = 10\)
  • get P-value
  • Count up how many of the P-values are 0.05 or less.

In code

library(tidyverse)
tibble(sim = 1:1000) %>% 
  rowwise() %>% 
  mutate(my_sample = list(rnorm(15, 8, 4))) %>% 
  mutate(t_test = list(t.test(my_sample, mu = 10))) %>% 
  mutate(p_val = t_test$p.value) %>% 
  count(p_val <= 0.05)

We correctly rejected 422 times out of 1000, so the estimated power is 0.422.

Aside: Try again with bigger sample

tibble(sim = 1:1000) %>% 
  rowwise() %>% 
  mutate(my_sample = list(rnorm(40, 8, 4))) %>% 
  mutate(t_test = list(t.test(my_sample, mu = 10))) %>% 
  mutate(p_val = t_test$p.value) %>% 
  count(p_val <= 0.05)

Calculating power

  • Simulation approach very flexible: will work for any test. But answer different each time because of randomness.
  • In some cases, for example 1-sample and 2-sample t-tests, power can be calculated.
  • power.t.test. Input delta is difference between null and true mean:
power.t.test(n = 40, delta = 10-8, sd = 4, 
             type = "one.sample")

Results

power.t.test(n = 15, delta = 10-8, sd = 4, 
             type = "one.sample")

     One-sample t test power calculation 

              n = 15
          delta = 2
             sd = 4
      sig.level = 0.05
          power = 0.4378466
    alternative = two.sided

Comparison of results

Method Power
Simulation 0.422
power.t.test 0.4378
  • Simulation power is similar to calculated power; to get more accurate value, repeat more times (eg. 10,000 instead of 1,000), which takes longer.
  • CI for power based on simulation approx. \(0.42 \pm 0.03\).
  • With this small a sample size, the power is not great. With a bigger sample, the sample mean should be closer to 8 most of the time, so would reject \(H_0 : \mu = 10\) more often.

Calculating required sample size

  • Often, when planning a study, we do not have a particular sample size in mind. Rather, we want to know how big a sample to take. This can be done by asking how big a sample is needed to achieve a certain power.
  • The simulation approach does not work naturally with this, since you have to supply a sample size.
    • For that, you try different sample sizes until you get power close to what you want.
  • For the power-calculation method, you supply a value for the power, but leave the sample size missing.
  • Re-use the same problem: \(H_0 : \mu = 10\) against 2-sided alternative, true \(\mu = 8\), \(\sigma = 4\), but now aim for power 0.80.

Using power.t.test

  • No n=, replaced by a power=:
power.t.test(power=0.80, delta=10-8, sd=4, type="one.sample")

     One-sample t test power calculation 

              n = 33.3672
          delta = 2
             sd = 4
      sig.level = 0.05
          power = 0.8
    alternative = two.sided
  • Sample size must be a whole number, so round up to 34 (to get at least as much power as you want).

One-sided test

power.t.test(power=0.80, delta=10-8, sd=4, 
             type="one.sample", alternative = "one.sided")

     One-sample t test power calculation 

              n = 26.13751
          delta = 2
             sd = 4
      sig.level = 0.05
          power = 0.8
    alternative = one.sided

By simulation

Try a sample size and see what power you get. Here’s \(n = 15\) from before:

tibble(sim = 1:1000) %>% 
  rowwise() %>% 
  mutate(my_sample = list(rnorm(15, 8, 4))) %>% 
  mutate(t_test = list(t.test(my_sample, mu = 10))) %>% 
  mutate(p_val = t_test$p.value) %>% 
  count(p_val <= 0.05)

To get power 0.80, two-sided, need a bigger sample.

To get a bigger power

How much bigger? No idea. Make any guess. What about \(n = 50\)?

tibble(sim = 1:1000) %>% 
  rowwise() %>% 
  mutate(my_sample = list(rnorm(50, 8, 4))) %>% 
  mutate(t_test = list(t.test(my_sample, mu = 10))) %>% 
  mutate(p_val = t_test$p.value) %>% 
  count(p_val <= 0.05)

Power now too big.

Try again

sample size between 15 and 50, say \(n = 30\):

tibble(sim = 1:1000) %>% 
  rowwise() %>% 
  mutate(my_sample = list(rnorm(30, 8, 4))) %>% 
  mutate(t_test = list(t.test(my_sample, mu = 10))) %>% 
  mutate(p_val = t_test$p.value) %>% 
  count(p_val <= 0.05)

Now a little too small, hence right answer between 30 and 50, closer to 30.

One last try (\(n = 35\))

tibble(sim = 1:1000) %>% 
  rowwise() %>% 
  mutate(my_sample = list(rnorm(35, 8, 4))) %>% 
  mutate(t_test = list(t.test(my_sample, mu = 10))) %>% 
  mutate(p_val = t_test$p.value) %>% 
  count(p_val <= 0.05)

But…

… simulation has randomness: limit to how close you can get.

Rule of thumb: with 1000 simulations, estimated power within 0.03 (3%).

Power curves

  • Rather than calculating power for one sample size, or sample size for one power, might want a picture of relationship between sample size and power.
  • Or, likewise, picture of relationship between difference between true and null-hypothesis means and power.
  • Called power curve.
  • Build and plot it yourself.

Building it 1/2

  • If you feed power.t.test a collection (“vector”) of values, it will do calculation for each one.
  • Do power for variety of sample sizes, from 10 to 100 in steps of 10:
ns <- seq(10,100,10)
ns
 [1]  10  20  30  40  50  60  70  80  90 100

Building it 2/2

  • Calculate powers:
ans<- power.t.test(n=ns, delta=10-8, sd=4, type="one.sample")
ans

     One-sample t test power calculation 

              n = 10, 20, 30, 40, 50, 60, 70, 80, 90, 100
          delta = 2
             sd = 4
      sig.level = 0.05
          power = 0.2928286, 0.5644829, 0.7539627, 0.8693979, 0.9338976, 0.9677886, 0.9847848, 0.9929987, 0.9968496, 0.9986097
    alternative = two.sided

Just the power

ans$power
 [1] 0.2928286 0.5644829 0.7539627 0.8693979 0.9338976
 [6] 0.9677886 0.9847848 0.9929987 0.9968496 0.9986097

Building a plot (1/2)

  • Make a data frame out of the values to plot:
d <- tibble(n=ns, power=ans$power)
d

Building a plot (2/2)

  • Plot these as points joined by lines, and add horizontal line at 1 (maximum power):
g <-   ggplot(d, aes(x = n, y = power)) + geom_point() + 
  geom_line() + 
  geom_hline(yintercept = 1, linetype = "dashed")

The power curve

g

Another way to do it:

tibble(n=ns) %>% rowwise() %>%
  mutate(power_output = 
           list(power.t.test(n = n, delta = 10-8, sd = 4, 
                             type = "one.sample"))) %>% 
  mutate(power = power_output$power) %>% 
  ggplot(aes(x=n, y=power)) + geom_point() + geom_line() +
    geom_hline(yintercept=1, linetype="dashed") -> g2

The power curve done the other way

g2

Power curves for means

  • Can also investigate power as it depends on what the true mean is (the farther from null mean 10, the higher the power will be).
  • Investigate for two different sample sizes, 15 and 30.
  • First make all combos of mean and sample size:
means <- seq(6,10,0.5)
means
[1]  6.0  6.5  7.0  7.5  8.0  8.5  9.0  9.5 10.0
ns <- c(15,30)
ns
[1] 15 30
combos <- crossing(mean=means, n=ns)

The combos

combos

Calculate and plot

  • Calculate the powers, carefully:
ans <- with(combos, power.t.test(n=n, delta=10-mean, sd=4, 
                              type="one.sample"))
ans$power
 [1] 0.94908647 0.99956360 0.88277128 0.99619287
 [5] 0.77070660 0.97770385 0.61513033 0.91115700
 [9] 0.43784659 0.75396272 0.27216777 0.51028173
[13] 0.14530058 0.26245348 0.06577280 0.09719303
[17] 0.02500000 0.02500000

Make a data frame to plot

pulling things from the right places:

d <- tibble(n=factor(combos$n), mean=combos$mean, 
            power=ans$power)
d

then make the plot:

g  <-  ggplot(d, aes(x = mean, y = power, colour = n)) +
  geom_point() + geom_line() +
  geom_hline(yintercept = 1, linetype = "dashed") +
  geom_vline(xintercept = 10, linetype = "dotted")

The power curves

g

Comments

  • When mean=10, that is, the true mean equals the null mean, \(H_0\) is actually true, and the probability of rejecting it then is \(\alpha = 0.05\).
  • As the null gets more wrong (mean decreases), it becomes easier to correctly reject it.
  • The blue power curve is above the red one for any mean < 10, meaning that no matter how wrong \(H_0\) is, you always have a greater chance of correctly rejecting it with a larger sample size.
  • Previously, we had \(H_0 : \mu = 10\) and a true \(\mu = 8\), so a mean of 8 produces power 0.42 and 0.80 as shown on the graph.
  • With \(n = 30\), a true mean that is less than about 7 is almost certain to be correctly rejected. (With \(n = 15\), the true mean needs to be less than 6.)

Two-sample power

  • For kids learning to read, had sample sizes of 22 (approx) in each group
  • and these group SDs:
kids %>% group_by(group) %>% 
  summarize(n=n(), s=sd(score))

Setting up

  • suppose a 5-point improvement in reading score was considered important (on this scale)
  • in a 2-sample test, nul(difference of) mean is zero, so delta is true difference in means
  • what is power for these sample sizes, and what sample size would be needed to get power up to 0.80?
  • SD in both groups has to be same in power.t.test, so take as 14.

Calculating power for sample size 22 (per group)

power.t.test(n=22, delta=5, sd=14, type="two.sample", 
             alternative="one.sided")

     Two-sample t test power calculation 

              n = 22
          delta = 5
             sd = 14
      sig.level = 0.05
          power = 0.3158199
    alternative = one.sided

NOTE: n is number in *each* group

sample size for power 0.8

power.t.test(power=0.80, delta=5, sd=14, type="two.sample", 
             alternative="one.sided")

     Two-sample t test power calculation 

              n = 97.62598
          delta = 5
             sd = 14
      sig.level = 0.05
          power = 0.8
    alternative = one.sided

NOTE: n is number in *each* group

Comments

  • The power for the sample sizes we have is very small (to detect a 5-point increase).
  • To get power 0.80, we need 98 kids in each group!