# Discussion of Other Tests

The following descriptions provide an overview of other hypothesis
tests available in the Analyst Application.

*One-Sample Z-Test for a Mean*

In the One-Sample Z-Test for a Mean task, you can test whether the
mean of a population is equal to the value you specify in the null
hypothesis. This test is appropriate when the population standard
deviation or variance is known, and your data are either normally
distributed or you have a large number of observations. Generally, a
sample size of at least 30 is considered to be sufficient.
The default output from the test includes summary statistics for the
selected variable, the *Z* statistic, and the associated *
p*-value.

*One-Sample Test for a Proportion*

In the One-Sample Test for a Proportion task, you can test whether
the proportion of a population giving a certain response is equal to
the proportion you specify in the null hypothesis.
The default output from this test provides a frequency table of
responses versus the analysis variable, the observed proportion, the
*Z* statistic, and the associated *p*-value.

*One-Sample Test for a Variance*

In the One-Sample Test for a Variance task, you can test whether the
variance of a population is equal to the value you specify
in the null hypothesis.
The default output from this test includes summary statistics for the
selected variable, the chi-square statistic, and the associated *
p*-value.

*Two-Sample t-Test for Means*

In the Two-Sample t-Test for Means task, you can test whether the
means of two populations are equal or, optionally, whether they
differ by a specified amount. Two-sample data arise when two
independent samples are observed, possibly with different sample
sizes. Note that, if the two samples are not independent, the two-sample
*t*-test is inappropriate and you should use instead the
Two-Sample Paired t-Test for Means task (see the section "Paired t-test" for
more information).
The default output from the test includes summary statistics for the
two samples, two *t* statistics, and the associated
*p*-values. The first *t* statistic assumes the
population variances of the two groups are equal; the second statistic
is an approximate *t* statistic and should be used when the
population variances of the two groups are potentially unequal.

Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.