If the Z Score of a test is equal to zero then the raw score of the test is equal to the mean. Z Score = (Raw Score - Mean Score) / Standard Deviation
Because under the null hypothesis of no difference, the appropriate test statistic can be shown to have a t-distribution with the relevant degrees of freedom. So you use the t-test to see how well the observed test statistic fits in with a t-distribution.
In order to know the z-score, given a test score, you must also know the mean and the standard deviation. Please restate the question.
If you already have your p-value, compare it with 0.05. If the p-value is less than an alpha of 0.05, the t-test is significant. If it is above 0.05, the t-test is not significant.
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t test, because the z test requires knowing the population standard deviation and that's rare. The t test embodies an estimate of the standard deviation.
a t test is used inplace of a z-test when the population standard deviation is unknown.
It depends on the population.Use t-test for a small population, N < 30; otherwiase, apply z-test or when N>=30.
You use a z test when you are testing a hypothesis that is using proportions You use a t test when you are testing a hypothesis that is using means
When we use a z-test, we know the population mean and standard deviation. When we use a t-test, we do not know the population standard deviation and thus must estimate this using the sample data that we have collected. If you look at your z-table and t-table, tcrit for df(infinity) = zcrit because at df(infinity) we would have an entire population and no longer need an estimate.
When the sample size is greater than 30
When the sample size is greater than 30
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No, the Z-test is not the same as a Z-score. The Z-test is where you take the Z-score and compare it to a critical value to determine if the null hypothesis will be rejected or fail to be rejected.
The answer depends on what the test statistic is: a t-statistic, z-score, chi square of something else.
When you don't have the population standard deviation, but do have the sample standard deviation. The Z score will be better to do as long as it is possible to do it.
Whereas a t-test is used for n30, where n=sample size. n < 30 or n > 30 is not entirely arbitrary; it is intended to indicate that n must be sufficiently large to use the normal distribution. In some cases, n must be greater than 50. Note, both the t-test and the z-test can only be used if the distribution from which the sample is being drawn is a normal distribution. A z-test can be used even if the distribution is not normal (but is not severely skewed) if n>30, in which case, we can safely assume that the distribution is normal.