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Z-Test

A Z-test is a statistical test used to determine whether there is a significant difference between sample and population means, or between the means of two samples, when the population variance is known. Z-tests are commonly used in hypothesis testing to compare the observed data with what would be expected under the null hypothesis. In finance, Z-tests can be applied to compare expected and actual returns, or to test the significance of financial metrics.

Example

A financial analyst uses a Z-test to determine whether the average return of a mutual fund differs significantly from the average market return.

Key points

• A statistical test used to determine if there is a significant difference between sample and population means.

• Assumes that the population variance is known and follows a normal distribution.

• Commonly used in hypothesis testing and financial analysis to compare data.

Quick Answers to Curious Questions

It is used to compare the mean of a sample (such as returns) to a population mean, helping assess whether the observed performance is statistically significant.

A Z-test is used when the population variance is known and the sample size is large, while a T-test is used when the population variance is unknown or the sample size is smaller.

Answer 3:It helps determine whether observed data significantly deviates from the expected outcome under the null hypothesis, allowing analysts to accept or reject the hypothesis.