`simple_htest.Rd`

Performs one or two sample t-tests or Wilcoxon-Mann-Whitney rank-based tests with expanded options compared to t.test, brunner_munzel, or wilcox.test.

```
simple_htest(
x,
...,
paired = FALSE,
alternative = c("two.sided", "less", "greater", "equivalence", "minimal.effect"),
mu = 0,
alpha = 0.05
)
# S3 method for default
simple_htest(
x,
y = NULL,
test = c("t.test", "wilcox.test", "brunner_munzel"),
paired = FALSE,
alternative = c("two.sided", "less", "greater", "equivalence", "minimal.effect"),
mu = 0,
alpha = 0.05,
...
)
# S3 method for formula
simple_htest(formula, data, subset, na.action, ...)
```

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

- ...
further arguments to be passed to or from methods.

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

- alternative
a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater", "less", "equivalence" (TOST), or "minimal.effect" (TOST). You can specify just the initial letter.

- mu
a number specifying an optional parameter used to form the null hypothesis. See ‘Details’.

- alpha
alpha level (default = 0.05)

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

- test
a character string specifying what type of hypothesis test to use. Options are limited to "wilcox.test", "t.test", or "brunner_munzel". You can specify just the initial letter.

- formula
a formula of the form lhs ~ rhs where lhs is a numeric variable giving the data values and rhs either 1 for a one-sample or paired test or a factor with two levels giving the corresponding groups. If lhs is of class "Pair" and rhs is 1, a paired test is done.

- 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 NAs. Defaults to getOption("na.action").

A list with class `"htest"`

containing the following components:

statistic: the value of the t-statistic.

parameter: the degrees of freedom for the t-statistic.

p.value: the p-value for the test.

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

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

null.value: the specified hypothesized value of the mean or mean difference. May be 2 values.

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

alternative: a character string describing the alternative hypothesis.

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

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

The type of test, t-test/Wilcoxon-Mann-Whitney/Brunner-Munzel, can be selected with the `"test"`

argument.

`alternative = "greater"`

is the alternative that x is larger than y (on average).
If `alternative = "equivalence"`

then the alternative is that the difference between x and y is between the two null values `mu`

..
If `alternative = "minimal.effect"`

then the alternative is that the difference between x and y is less than the lowest null value or greater than the highest.

For more details on each possible test (brunner_munzel, stats::t.test, or stats::wilcox.test), please read their individual documentation.

Other TOST:
`boot_log_TOST()`

,
`boot_t_TOST()`

,
`t_TOST()`

,
`tsum_TOST()`

,
`wilcox_TOST()`

Other htest:
`as_htest()`

,
`htest-helpers`

```
data(mtcars)
simple_htest(mpg ~ am,
data = mtcars,
alternative = "e",
mu = 3)
#>
#> Welch Two Sample t-test
#>
#> data: mpg by am
#> t = -2.2072, df = 18.332, p-value = 0.9799
#> alternative hypothesis: equivalence
#> null values:
#> difference in means difference in means
#> -3 3
#> 90 percent confidence interval:
#> -10.576623 -3.913256
#> sample estimates:
#> mean of x mean of y
#> 17.14737 24.39231
#>
```