A hypothesis test is a formal statistical test we use to reject or fail to reject some statistical hypothesis.
This tutorial explains how to perform the following hypothesis tests in R:
- One sample t-test
- Two sample t-test
- Paired samples t-test
We can use the t.test() function in R to perform each type of test:
#one sample t-testt.test(x, y = NULL, alternative = c("two.sided", "less", "greater"), mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95, …)
where:
- x, y: The two samples of data.
- alternative: The alternative hypothesis of the test.
- mu: The true value of the mean.
- paired: Whether to perform a paired t-test or not.
- var.equal: Whether to assume the variances are equal between the samples.
- conf.level: The confidence level to use.
The following examples show how to use this function in practice.
Example 1: One Sample t-test in R
A one sample t-test is used to test whether or not the mean of a population is equal to some value.
For example, suppose we want to know whether or not the mean weight of a certain species of some turtle is equal to 310 pounds. We go out and collect a simple random sample of turtles with the following weights:
Weights: 300, 315, 320, 311, 314, 309, 300, 308, 305, 303, 305, 301, 303
The following code shows how to perform this one sample t-test in R:
#define vector of turtle weightsturtle_weights <- c(300, 315, 320, 311, 314, 309, 300, 308, 305, 303, 305, 301, 303)#perform one sample t-testt.test(x = turtle_weights, mu = 310)One Sample t-testdata: turtle_weightst = -1.5848, df = 12, p-value = 0.139alternative hypothesis: true mean is not equal to 31095 percent confidence interval: 303.4236 311.0379sample estimates:mean of x 307.2308
From the output we can see:
- t-test statistic: -1.5848
- degrees of freedom:12
- p-value:0.139
- 95% confidence interval for true mean:[303.4236, 311.0379]
- mean of turtle weights:307.230
Since the p-value of the test (0.139) is not less than .05, we fail to reject the null hypothesis.
This means we do not have sufficient evidence to say that the mean weight of this species of turtle is different from 310 pounds.
Example 2: Two Sample t-test in R
A two sample t-test is used to test whether or not the means of two populations are equal.
For example, suppose we want to know whether or not the mean weight between two different species of turtles is equal. To test this, we collect a simple random sample of turtles from each species with the following weights:
Sample 1: 300, 315, 320, 311, 314, 309, 300, 308, 305, 303, 305, 301, 303
Sample 2: 335, 329, 322, 321, 324, 319, 304, 308, 305, 311, 307, 300, 305
The following code shows how to perform this two sample t-test in R:
#define vector of turtle weights for each samplesample1 <- c(300, 315, 320, 311, 314, 309, 300, 308, 305, 303, 305, 301, 303)sample2 <- c(335, 329, 322, 321, 324, 319, 304, 308, 305, 311, 307, 300, 305)#perform two sample t-testt.test(x = sample1, y = sample2)Welch Two Sample t-testdata: sample1 and sample2t = -2.1009, df = 19.112, p-value = 0.04914alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -14.73862953 -0.03060124sample estimates:mean of x mean of y 307.2308 314.6154
From the output we can see:
- t-test statistic: -2.1009
- degrees of freedom:19.112
- p-value:0.04914
- 95% confidence interval for true mean difference: [-14.74, -0.03]
- mean of sample 1 weights: 307.2308
- mean of sample 2 weights:314.6154
Since the p-value of the test (0.04914) is less than .05, we reject the null hypothesis.
This means we have sufficient evidence to say that the mean weight between the two species is not equal.
Example 3: Paired Samples t-test in R
A paired samples t-test is used to compare the means of two samples when each observation in one sample can be paired with an observation in the other sample.
For example, suppose we want to know whether or not a certain training program is able to increase the max vertical jump (in inches) of basketball players.
To test this, we may recruit a simple random sample of 12 college basketball players and measure each of their max vertical jumps. Then, we may have each player use the training program for one month and then measure their max vertical jump again at the end of the month.
The following data shows the max jump height (in inches) before and after using the training program for each player:
Before: 22, 24, 20, 19, 19, 20, 22, 25, 24, 23, 22, 21
After: 23, 25, 20, 24, 18, 22, 23, 28, 24, 25, 24, 20
The following code shows how to perform this paired samples t-test in R:
#define before and after max jump heightsbefore <- c(22, 24, 20, 19, 19, 20, 22, 25, 24, 23, 22, 21)after <- c(23, 25, 20, 24, 18, 22, 23, 28, 24, 25, 24, 20)#perform paired samples t-testt.test(x = before, y = after, paired = TRUE)Paired t-testdata: before and aftert = -2.5289, df = 11, p-value = 0.02803alternative hypothesis: true difference in means is not equal to 095 percent confidence interval: -2.3379151 -0.1620849sample estimates:mean of the differences -1.25
From the output we can see:
- t-test statistic: -2.5289
- degrees of freedom:11
- p-value:0.02803
- 95% confidence interval for true mean difference: [-2.34, -0.16]
- mean difference between before and after: -1.25
Since the p-value of the test (0.02803) is less than .05, we reject the null hypothesis.
This means we have sufficient evidence to say that the mean jump height before and after using the training program is not equal.
Additional Resources
Use the following online calculators to automatically perform various t-tests:
One Sample t-test Calculator
Two Sample t-test Calculator
Paired Samples t-test Calculator
