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# Continuous Data

This section describes tests that apply to continuous random variables. Many important measurements fall into this category, such as times, dollar amounts, and chemical concentrations.

## Normal Distribution-Based Tests

We’ll start off by showing how to use some common statistical tests that assume the underlying data is normally distributed. Normal distributions occur frequently in nature, so this is often a good assumption.[50]

### Comparing means

Suppose that you designed an experiment to show that some effect is true. You have collected some data and now want to know if the data proves your hypothesis. One common question is to ask if the mean of the experimental data is close to what the experimenter expected; this is called the null hypothesis. Alternately, the experimenter may calculate the probability that an alternative hypothesis was true. Specifically, suppose that you have a set of observations x1, x2, ..., xn with experimental mean μ and want to know if the experimental mean is different from the null hypothesis mean μ0. Furthermore, assume that the observations are normally distributed. To test the validity of the hypothesis, you can use a t-test. In R, you would use the function `t.test`:

```## Default S3 method:
t.test(x, y = NULL,
alternative = c("two.sided", "less", "greater"),
mu = 0, paired = FALSE, var.equal = FALSE,
conf.level = 0.95, ...)```

Here is a description of the arguments to the `t.test` function.

ArgumentDescriptionDefault
xA numeric vector of data values. ...