A z-test is a statistical method used in Six Sigma to determine whether there is a significant difference between sample and population means, or between the means of two samples. It is applicable when the population variance is known and the sample size is large (typically over 30). The z-test calculates a z-score, which represents the number of standard deviations a data point is from the mean. By comparing the z-score to a standard normal distribution, you can assess the likelihood of the observed difference occurring by chance. Z-tests are crucial for hypothesis testing and making data-driven decisions in process improvement.
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