Editing Z-test (section)

== Conditions ==

For the ''Z''-test to be applicable, certain conditions must be met.

* [[Nuisance parameter]]s should be known, or estimated with high accuracy (an example of a nuisance parameter would be the [[standard deviation]] in a one-sample location test). ''Z''-tests focus on a single parameter, and treat all other unknown parameters as being fixed at their true values. In practice, due to [[Slutsky's theorem]], "plugging in" [[consistent estimator|consistent]] estimates of nuisance parameters can be justified. However, if the sample size is not large enough for these estimates to be reasonably accurate, the ''Z''-test may not perform well.
* The test statistic should follow a [[normal distribution]]. Generally, one appeals to the [[central limit theorem]] to justify assuming that a test statistic varies normally. There is a great deal of statistical research on the question of when a test statistic varies approximately normally. If the variation of the test statistic is strongly non-normal, a ''Z''-test should not be used.

If estimates of nuisance parameters are plugged in as discussed above, it is important to use estimates appropriate for the way the data were [[sampling (statistics)|sampled]]. In the special case of ''Z''-tests for the one or two sample location problem, the usual sample standard deviation is only appropriate if the data were collected as an independent sample.

In some situations, it is possible to devise a test that properly accounts for the variation in plug-in estimates of nuisance parameters. In the case of one and two sample location problems, a [[t-test|''t''-test]] does this.