1 Download the Dataset

The code below downloads the data set and assigns the dataset to a variable we create and call cps04.

cps04 <- read.csv(url("http://murraylax.org/datasets/cps04.csv"))

The dataset cps04 contains a variable called ahe, which stands for average hourly earnings.

2 Compute the Sample Mean

mean(cps04$ahe)

## [1] 16.77115

The sample estimate for average hourly earnings for U.S. workers is 2004 is $16.77. This is not necessarily the population mean. Like every statistic, it includes a margin of error due to random sampling error.

3 Compute a 95% Confidence Interval

The confidence interval is a range of values for the population mean, based on our estimate of the sample mean, and an estimate for the margin of error due to random sampling.

The function t.test computes a number of statistics and statistical tests for a variable, including a confidence interval. In the code below, we use the function to compute our confidence interval and assign all the resulting output to a new variable we call ahestats.

ahestats <- t.test(cps04$ahe, conf.level = 0.95)

The output of t.test that we assigned to variable ahestats is a list which includes an item called conf.int.

Let’s call this item to report our confidence interval:

ahestats$conf.int

## [1] 16.57902 16.96328
## attr(,"conf.level")
## [1] 0.95

The confidence interval for average hourly earnings for U.S. workers is 2004 is $16.58 - $16.96. We can say with 95% confidence that this interval estimate includes the true population mean.

4 One Sample T-Test (One-tailed)

Suppose a politician claimed that the average earnings of American workers was more than $16.50 per hour. We know that the sample estimate is larger from above, but let’s test the hypothesis that the population mean is more than $16.50.

The appropriate statistical procedure is the One-sample T-test for a Mean which tests whether a single population mean is equal to or different than a particular value. Our null and alternative hypotheses for our one-sample t-test is given by the following:

Null hypothesis: \(\mu = 16.50\)
Alternative hypothesis: \(\mu > 16.50\)

The t.test function can also compute the one-sample t-test using the following code:

t.test(cps04$ahe, mu=16.50, alternative = "greater")

## 
##  One Sample t-test
## 
## data:  cps04$ahe
## t = 2.7665, df = 7985, p-value = 0.002839
## alternative hypothesis: true mean is greater than 16.5
## 95 percent confidence interval:
##  16.60992      Inf
## sample estimates:
## mean of x 
##  16.77115

The output of the test reveals a p-value equal to 0.00028. Since this is below 5%, we reject the null hypothesis and conclude that we do have statistical evidence that the population mean is greater than $16.50.

5 Two-Tailed Test

The previous example is a one-tailed test. That is, it involved an alternative hypothesis that looked for statistical evidence that the population parameter was in a particular direction away from the null hypothesized value (in the case above, greater than the null hypothesis).

A two tailed test instead tests an alternative hypothesis that simply says the population parameter is different than the null hypothesized value, leaving the possibility that it may be less than or may be greater than the value.

Let’s test the following two-tailed hypotheses:

Null hypothesis: \(\mu = 16.50\)
Alternative hypothesis: \(\mu \neq 16.50\)

Notice the \(\neq\) sign in the alternative hypothesis.

We use the t.test function again to compute the one-sample t-test using the following code:

t.test(cps04$ahe, mu=16.50, alternative="two.sided")

## 
##  One Sample t-test
## 
## data:  cps04$ahe
## t = 2.7665, df = 7985, p-value = 0.005679
## alternative hypothesis: true mean is not equal to 16.5
## 95 percent confidence interval:
##  16.57902 16.96328
## sample estimates:
## mean of x 
##  16.77115

We can see from the output that the p-value is equal to 0.0057. Since this is below 5%, we reject the null hypothesis and conclude that we do have statistical evidence that the population mean is different than $16.50.