Z-Test

What Is a Z-Test?

A z-test is a statistical test used to determine whether two population means are different when the variances are known and the sample size is large.

The test statistic is assumed to have a normal distribution, and irritation parameters, for example, standard deviation ought to be known for an accurate z-test to be performed.

Understanding Z-Tests

The z-test is likewise a hypothesis test in which the z-statistic follows a normal distribution. The z-test is best utilized for greater-than-30 samples in light of the fact that, under the central limit theorem, as the number of samples gets larger, the samples are viewed as approximately normally distributed.

While conducting a z-test, the null and alternative hypotheses, alpha and z-score ought to be stated. Next, the test statistic ought to be calculated, and the results and end stated. A z-statistic, or z-score, is a number representing the number of standard deviations above or below the mean population a score that derived from a z-test is.

Instances of tests that can be conducted as z-tests incorporate a one-sample location test, a two-sample location test, a paired difference test, and a maximum probability estimate. Z-tests are closely related to t-tests, but t-tests are best performed when an experiment has a small sample size. Likewise, t-tests expect the standard deviation is obscure, while z-tests accept it is known. On the off chance that the standard deviation of the population is obscure, the assumption of the sample variance approaching the population variance is made.

One-Sample Z-Test Example

Expect an investor wishes to test whether the average daily return of a stock is greater than 3%. A simple random sample of 50 returns is calculated and has an average of 2%. Expect the standard deviation of the returns is 2.5%. Therefore, the null hypothesis is the point at which the average, or mean, is equivalent to 3%.

Alternately, the alternative hypothesis is whether the mean return is greater or under 3%. Expect an alpha of 0.05% is selected with a two-tailed test. Consequently, there is 0.025% of the samples in each tail, and the alpha has a critical value of 1.96 or - 1.96. Assuming the value of z is greater than 1.96 or not exactly - 1.96, the null hypothesis is rejected.

The value for z is calculated by subtracting the value of the average daily return selected for the test, or 1% in this case, from the noticed average of the samples. Next, partition the resulting value by the standard deviation isolated by the square root of the number of noticed values.

Therefore, the test statistic is:

(0.02 - 0.01) \u00f7 (0.025 \u00f7 \u221a 50) = 2.83

The investor rejects the null hypothesis since z is greater than 1.96 and presumes that the average daily return is greater than 1%.

Highlights

• A z-test is a hypothesis test in which the z-statistic follows a normal distribution.
• A z-test is a statistical test to determine whether two population means are different when the variances are known and the sample size is large.
• A z-statistic, or z-score, is a number representing the result from the z-test.
• Z-tests accept the standard deviation is known, while t-tests expect it is obscure.
• Z-tests are closely related to t-tests, but t-tests are best performed when an experiment has a small sample size.

FAQ

What Is Central Limit Theorem (CLT)?

In the study of probability theory, the central limit theorem (CLT) states that the distribution of sample approximates a normal distribution (otherwise called a "ringer bend") as the sample size expands, expecting that all samples are identical in size, and no matter what the population distribution shape. Sample sizes equivalent to or greater than 30 are thought of as sufficient for the CLT to predict the characteristics of a population accurately.

What Is a Z-Score?

A z-score, or z-statistic, is a number representing the number of standard deviations above or below the mean population the score that derived from a z-test is. Essentially, it is a mathematical measurement that depicts a value's relationship to the mean of a group of values. If a z-score is 0, it indicates that the data point's score is identical to the mean score. A z-score of 1.0 would indicate a value that is one standard deviation from the mean. Z-scores might be positive or negative, with a positive value indicating the score is over the mean and a negative score indicating it is below the mean.

What's the Difference Between a T-Test and Z-Test?

Z-tests are closely related to t-tests, but t-tests are best performed when an experiment has a small sample size, under 30. Additionally, t-tests accept the standard deviation is obscure, while z-tests expect it is known. In the event that the standard deviation of the population is obscure, but the sample size is greater than or equivalent to 30, then the assumption of the sample variance approaching the population variance is made while utilizing the z-test.