# The Complete Guide: Hypothesis Testing in R

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-test
t.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 weights
turtle_weights <- c(300, 315, 320, 311, 314, 309, 300, 308, 305, 303, 305, 301, 303)

#perform one sample t-test
t.test(x = turtle_weights, mu = 310)

One Sample t-test

data:  turtle_weights
t = -1.5848, df = 12, p-value = 0.139
alternative hypothesis: true mean is not equal to 310
95 percent confidence interval:
303.4236 311.0379
sample 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 sample
sample1 <- 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-test
t.test(x = sample1, y = sample2)

Welch Two Sample t-test

data:  sample1 and sample2
t = -2.1009, df = 19.112, p-value = 0.04914
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-14.73862953  -0.03060124
sample 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 heights
before <- 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-test
t.test(x = before, y = after, paired = TRUE)

Paired t-test

data:  before and after
t = -2.5289, df = 11, p-value = 0.02803
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-2.3379151 -0.1620849
sample 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.