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. It can also be used to compare one mean to a hypothesized value.
The data must approximately fit a normal distribution, otherwise the test doesn't work. Parameters such as variance and standard deviation should be calculated for a z-test to be performed.
Key Takeaways
- A z-test is a statistical test to determine whether two population means are different or to compare one mean to a hypothesized value when the variances are known and the sample size is large.
- A z-test is a hypothesis test for data that follows a normal distribution.
- A z-statistic, or z-score, is a number representing the result from the z-test.
- Z-tests are closely related to t-tests, but t-tests are best performed when an experiment has a small sample size.
- Z-tests assume the standard deviation is known, while t-tests assume it is unknown.
Understanding Z-Tests
The z-test is also a hypothesis test in which the z-statistic follows a normal distribution. The z-test is best used for greater-than-30 samples because, under the central limit theorem, as the number of samples gets larger, the samples are considered to be approximately normally distributed.
When conducting a z-test, the null and alternative hypotheses, and alpha level should be stated. The z-score, also called a test statistic, should be calculated, and the results and conclusion stated. A z-statistic, or z-score, is a number representing how many standard deviations above or below the mean population a score derived from a z-test is.
Examples of tests that can be conducted as z-tests include a one-sample location test, a two-sample location test, a paired difference test, and a maximum likelihood estimate. Z-tests are closely related to t-tests, but t-tests are best performed when an experiment has a small sample size. Also, t-tests assume the standard deviation is unknown, while z-tests assume it is known. If the standard deviation of the population is unknown, the assumption of the sample variance equaling the population variance is made.
Formula for Z-Score
The Z-score is calculated with the formula:
z = ( x - μ ) / σ
Where:
- z = Z-score
- x = the value being evaluated
- μ = the mean
- σ = the standard deviation
One-Sample Z-Test Example
Assume 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%. Assume the standard deviation of the returns is 2.5%. Therefore, the null hypothesis is when the average, or mean, is equal to 3%.
Conversely, the alternative hypothesis is whether the mean return is greater or less than 3%. Assume 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. If the value of z is greater than 1.96 or less than -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 3% in this case, from the observed average of the samples. Next, divide the resulting value by the standard deviation divided by the square root of the number of observed values.
Therefore, the test statistic is:
(0.02 - 0.03) ÷ (0.025 ÷ √ 50) = -2.83
The investor rejects the null hypothesis since z is less than -1.96 and concludes that the average daily return is less than 3%.
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 the data consists of a small sample size, i.e., less than 30. Also, t-tests assume the standard deviation is unknown, while z-tests assume it is known.
When Should You Use a Z-Test?
If the standard deviation of the population is known and the sample size is greater than or equal to 30, the z-test can be used. Regardless of the sample size, if the population standard deviation is unknown, a t-test should be used instead.
What Is a Z-Score?
A z-score, or z-statistic, is a number representing how many standard deviations above or below the mean population the score derived from a z-test is. Essentially, it is a numerical measurement that describes 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 may be positive or negative, with a positive value indicating the score is above the mean and a negative score indicating it is below the mean.
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 (also known as a “bell curve”) as the sample size becomes larger, assuming that all samples are identical in size, and regardless of the population distribution shape. Sample sizes equal to or greater than 30 are considered sufficient for the CLT to predict the characteristics of a population accurately. The z-test's fidelity relies on the CLT holding.
What Are the Assumptions of the Z-Test?
For a z-test to be effective, the population must be normally distributed, and the samples must have the same variance. In addition, all data points should be independent of one another.
The Bottom Line
A z-test is used in hypothesis testing to evaluate whether a finding or association is statistically significant or not. In particular, it tests whether two means are the same (the null hypothesis). A z-test can only be used if the population standard deviation is known and the sample size is 30 data points or larger. Otherwise, a t-test should be employed.
