z.test {NCStats} R Documentation

## Z test for known population standard deviation

### Description

Compute the test of hypothesis and compute confidence interval on the mean of a population when the standard deviation of the population is known.

### Usage

```  z.test(x, mu = 0, sd,
alternative = c("two.sided", "less", "greater"),
conf.level = 0.95, ...)
```

### Arguments

 `x` Vector of data values. `mu` Hypothesized mean of the population. `sd` Known standard deviation of the population. `alternative` Direction of the alternative hypothesis. `conf.level` Confidence level for the interval computation. `...` Additional arguments are silently ignored.

### Details

Most introductory statistical texts introduce inference by using the Z test and Z based confidence intervals based on knowing the population standard deviation. Most statistical packages do not include functions to do Z tests because the T test is usually more appropriate for real world situations. This function is meant to be used during that short period of learning when the student is learning about inference using Z procedures, but has not learned the T based procedures yet. Once the student has learned about the T distribution the `t.test()` function should be used instead of this one (but the syntax is very similar, so this function should be an appropriate introductory step to learning `t.test()`).

### Value

An object of class `htest` containing the results

### Note

This function should be used for learning only, real data should generally use `t.test()`.

### Author(s)

Greg Snow greg.snow@imail.org with a slight modification (removed the stdev argument) by the package author.

`t.test`
```x <- rnorm(25, 100, 5)