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New page: The '''Z-test''' is a statistical test used in inference which determines if the difference between a sample mean and the population mean is large enough to be statistically significan...
The '''Z-test''' is a [[statistical test]] used in inference which determines if the difference between a sample mean and the population mean is large enough to be statistically significant.
The Z-test is conducted by the following steps:
First the standard error of the mean is calculated:
:<math>\mathrm{SE} = \frac{\sigma}{\sqrt n}</math>
Then the formula for the Z-score for the Z-test is calculated by the formula:
:<math>z = \frac{x-\mu}{\mathrm{SE}}</math>
The Z-score is then compared to a Z-table. The Z-table indicates whether the calculated z-score is within the realm of chance or if the z-score is so different that the sample mean is unlikely to have happened by chance.
The Z-test is conducted by the following steps:
First the standard error of the mean is calculated:
:<math>\mathrm{SE} = \frac{\sigma}{\sqrt n}</math>
Then the formula for the Z-score for the Z-test is calculated by the formula:
:<math>z = \frac{x-\mu}{\mathrm{SE}}</math>
The Z-score is then compared to a Z-table. The Z-table indicates whether the calculated z-score is within the realm of chance or if the z-score is so different that the sample mean is unlikely to have happened by chance.