The Difference Between Z-Test and T-Test

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Z –Test: a statistical test used for inference (inference – is the act or process of deriving logical conclusions from evidence, statements, ideas, etc. known or assumed to be true) which determines if the difference between a sample mean and the population mean is large enough to be statistically significant, that is, if it is unlikely to have occurred by chance. The Z – test is used primarily with its standardized testing to determine if the test scores of a particular sample of test takers are within or outside of the standard performance of test taking. T – Test: gives an indication of the distinctiveness of two sets of measurements, and is then used to check whether two sets of measurements are in essence different and are usually determined through an experimental demonstration. The standard way of doing this is with the null- hypothesis that means (means – a type of average) of the two sets of measures are equal. An example of a Z – test: three people are given some wheat bread to eat and then there blood sugar count is tested a few hours later and the results are that their blood sugar count is hypothetical 100. The next day the same three people are given a Snickers candy bar to eat and then their blood sugar is tested a few hours later and the results is that there blood sugar count is hypothetically 50. The statistical results show that eating wheat products produces a higher blood sugar counts. An example of a T – test: a group of people who have lived in a city and are now considering retirement will most likely move into a gated community where as a group of people who have lived in the country or in a small town are most likely to live in a non-gated community.
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