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How to compute a confidence interval for the difference between two means when population variances are unknown (in R)

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Task

If we have samples from two independent populations and both of the population variances are unknown, how do we compute a confidence interval for the difference between the population means?

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Solution

We’re going to use some fake data here to illustrate how to make the confidence interval. Replace our fake data with your actual data if you use this code.

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sample.1 <- c(15, 10, 7, 22, 17, 14)
sample.2 <- c(9, 1, 11, 13, 3, 6)

In the example below, we specify var.equal = FALSE to indicate that we cannot assume that the variances are equal. If you know them to be equal in your situation, replace FALSE with TRUE.

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alpha <- 0.05       # replace with your chosen alpha (here, a 95% confidence level)
conf.interval <- t.test(sample.1, sample.2, var.equal = FALSE, conf.level = 1-alpha)
# If you need the upper and lower bounds later, store them in variables like this:
lower.bound <- conf.interval$conf.int[1]
upper.bound <- conf.interval$conf.int[2]
# Print out the lower and upper bounds
lower.bound
upper.bound

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[1] 0.5852484



[1] 13.41475

Our 95% confidence interval for the true difference between these population means is $[0.5852, 13.4147]$.

You can also see the test statistic and $p$-value by inspecting the result of the t.test function we ran above.

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conf.interval

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	Welch Two Sample t-test

data:  sample.1 and sample.2
t = 2.4363, df = 9.8554, p-value = 0.0354
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
  0.5852484 13.4147516
sample estimates:
mean of x mean of y 
14.166667  7.166667

Content last modified on 24 July 2023.

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Contributed by Elizabeth Czarniak (CZARNIA_ELIZ@bentley.edu)