Z-test
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The Z-test is a statistical test used in inference.
The test requires the following to be known:
- σ (the standard deviation of the population)
- μ (the mean of the population)
- x (the mean of the sample)
Other conditions to be met include knowing that your sample mean from a simple random sample of the population. If the sample came from a different sampling method, a different formula must be used. It must also be known that the population varies normally (i.e., the sampling distribution of the probabilities of possible values fits a standard normal curve). If it is not known that the population varies normally, if suffices to have a sufficiently large sample, generally agreed to be ≥ 30 or 40.
In actuality, knowing the true σ of a population is unrealistic, as it is impossible to measure every member of a population. It is more realistic to use a t-test, which uses the standard error obtained from the sample along with the t-distribution.
The formula is as follows:
- <math>z = \frac{\mu-x}{\sigma}<math>
External link
- Code/pseudo-code for Z-test at Google Groups (http://groups.google.ca/groups?selm=3757C73F.45B4675F%40geog.uu.nl)
Z test formula is actually as follows:
z = (x - mu)/sigma