Performs one and two sample t-tests on vectors of data.

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

# S3 method for formula
t.test(formula, data, subset, na.action, …)

x

a (non-empty) numeric vector of data values.

y

an optional (non-empty) numeric vector of data values.

alternative

a character string specifying the alternative
hypothesis, must be one of `"two.sided"`

(default),
`"greater"`

or `"less"`

. You can specify just the initial
letter.

mu

a number indicating the true value of the mean (or difference in means if you are performing a two sample test).

paired

a logical indicating whether you want a paired t-test.

var.equal

a logical variable indicating whether to treat the
two variances as being equal. If `TRUE`

then the pooled
variance is used to estimate the variance otherwise the Welch
(or Satterthwaite) approximation to the degrees of freedom is used.

conf.level

confidence level of the interval.

formula

a formula of the form `lhs ~ rhs`

where `lhs`

is a numeric variable giving the data values and `rhs`

a factor
with two levels giving the corresponding groups.

data

an optional matrix or data frame (or similar: see
`model.frame`

) containing the variables in the
formula `formula`

. By default the variables are taken from
`environment(formula)`

.

subset

an optional vector specifying a subset of observations to be used.

na.action

a function which indicates what should happen when
the data contain `NA`

s. Defaults to
`getOption("na.action")`

.

…

further arguments to be passed to or from methods.

A list with class `"htest"`

containing the following components:

the value of the t-statistic.

the degrees of freedom for the t-statistic.

the p-value for the test.

a confidence interval for the mean appropriate to the specified alternative hypothesis.

the estimated mean or difference in means depending on whether it was a one-sample test or a two-sample test.

the specified hypothesized value of the mean or mean difference depending on whether it was a one-sample test or a two-sample test.

the standard error of the mean (difference), used as denominator in the t-statistic formula.

a character string describing the alternative hypothesis.

a character string indicating what type of t-test was performed.

a character string giving the name(s) of the data.

The formula interface is only applicable for the 2-sample tests.

`alternative = "greater"`

is the alternative that `x`

has a
larger mean than `y`

.

If `paired`

is `TRUE`

then both `x`

and `y`

must
be specified and they must be the same length. Missing values are
silently removed (in pairs if `paired`

is `TRUE`

). If
`var.equal`

is `TRUE`

then the pooled estimate of the
variance is used. By default, if `var.equal`

is `FALSE`

then the variance is estimated separately for both groups and the
Welch modification to the degrees of freedom is used.

If the input data are effectively constant (compared to the larger of the two means) an error is generated.

```
# NOT RUN {
require(graphics)
t.test(1:10, y = c(7:20)) # P = .00001855
t.test(1:10, y = c(7:20, 200)) # P = .1245 -- NOT significant anymore
## Classical example: Student's sleep data
plot(extra ~ group, data = sleep)
## Traditional interface
with(sleep, t.test(extra[group == 1], extra[group == 2]))
## Formula interface
t.test(extra ~ group, data = sleep)
# }
```

Run the code above in your browser using DataLab