The size of the sample is an important factor in Z-Testing as the Z-Test follows a normal distribution and so should the data.
The sample size should be greater than 30. However, there are certain conditions that should be satisfied by the sample to run the Z-Test. Z-Test can be considered as a test statistic for a hypothesis test to calculate the P-value. Hence a sufficiently large data should be considered for a hypothesis test especially if the population is huge. As sample data provide us a door to the entire population, it should be large enough for us to arrive at a significant inference. When the population has a large size, and the sample data is small, we will not be able to draw an accurate conclusion from the test to prove our null hypothesis is false. In a hypothesis test, we are trying to reject a null hypothesis with the evidence that we should collect from sample data which represents only a portion of the population. Why do we use a large sample for conducting a hypothesis test? Since the Z-Test follows normal distribution under the null hypothesis, it is the most suitable test statistic for large sample data. This is because of the central limit theorem where when the sample gets larger, the distributed data graph starts resembling a bell curve and is considered to be distributed normally. Z-Test is usually used to conduct a hypothesis test when the sample size is greater than 30. Z-Test compares the mean of two large samples taken from a population when the variance is known. Z-Test is such a test statistic where we make use of the mean value and z score to determine the P-value. 
This random variable is used to calculate the P-value, which indicates how strong the evidence is against the null hypothesis.
Z-Test as Hypothesis TestĪ test statistic is a random variable that we calculate from the sample data to determine whether to reject the null hypothesis. We make use of the Z score and the Z table for running the Z-Test. This test is widely used to determine whether the mean of the two samples are different when the variance is known.
Z-Test is a test statistic commonly used in hypothesis test when the sample data is large.For carrying out the Z-Test, population parameters such as mean, variance, and standard deviation should be known.
See solutions, f.Z-Test is a statistical test which let’s us approximate the distribution of the test statistic under the null hypothesis using normal distribution.
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