Question

A t-test and a z-test are very similar but have different criteria for their use. What are two reasons why you would use a t test over a z test?

Answer 1

Solution :-

Z-Tests and T-Tests

Z-tests and t-tests are statistical methods involving data analysis that have applications in business, science, and many other disciplines. some of their differences and similarities as well as situations where one of these methods should be used over the other.

A z-test is a statistical test to help determine the probability that new data will be near the point for which a score was calculated.

Z-tests are statistical calculations that can be used to compare population means to a sample's. The z-score tells you how far, in standard deviations, a data point is from the mean or average of a data set. A z-test compares a sample to a defined population and is typically used for dealing with problems relating to large samples (n > 30). Z-tests can also be helpful when we want to test a hypothesis. Generally, they are most useful when the standard deviation is known.

Like z-tests, t-tests are calculations used to test a hypothesis, but they are most useful

A t-test is a statistical method used to see if two sets of data are significantly different.

1) when we need to determine if there is a statistically significant difference between two independent sample groups. In other words, a t-test asks whether a difference between the means of two groups is unlikely to have occurred because of random chance.

2) t-tests are most appropriate when dealing with problems with a limited sample size (n < 30).

Both z-tests and t-tests require data with a normal distribution, which means that the sample (or population) data is distributed evenly around the mean