One common test on samples is the mean test. The t-test is widely used
in this regard. For example, maybe you know that the average sales for
your company per a week is $500, and you just collected a couple of
weeks of sales data. You would like to conduct a hypothesis test as to
if your new sample is different than the avg $500. In this article, we
will learn how to use the `t-test`

to test the mean of a sample in R.

Continuing from our example, let’s say we have collected five weeks of
sales data. We know that the average sales per week is $500, so we will
conduct a test to see if our recent samples differ. We can use the
`t.test`

method to perform this test.

```
recent.sales = c(400, 600, 850, 550)
avg.sales = 500
t.test(recent.sales, mu=avg.sales)
```

```
##
## One Sample t-test
##
## data: recent.sales
## t = 1.069, df = 3, p-value = 0.3634
## alternative hypothesis: true mean is not equal to 500
## 95 percent confidence interval:
## 302.3094 897.6906
## sample estimates:
## mean of x
## 600
```

```
# One Sample t-test
# data: recent.sales
# t = 1.069, df = 3, p-value = 0.3634
# alternative hypothesis: true mean is not equal to 500
# 95 percent confidence interval:
# 302.3094 897.6906
# sample estimates:
# mean of x
# 600
```

There is quite a bit of information here, but the focus for now will be
on the `p-value`

. Since this number is higher than `.05`

, in this case
we will fail to reject the null hypothesis. That means, we do not have
enough evidence to say that our sample data is different from the
average.

Now, that we have conducted our first t-test. Let’s take a look at the
response object to see what other information is returned. We can use
the `names`

method to see which properties are on the return t-test.

```
result = t.test(recent.sales, mu=avg.sales)
names(result)
```

```
## [1] "statistic" "parameter" "p.value" "conf.int" "estimate"
## [6] "null.value" "stderr" "alternative" "method" "data.name"
```

You can see there are quite a bit of values returned. These are helpful
for different reasons. Let’s extract the `p.value`

and `conf.int`

for an
example.

`result$p.value`

`## [1] 0.3634261`

`result$conf.int`

```
## [1] 302.3094 897.6906
## attr(,"conf.level")
## [1] 0.95
```