Healthcare quality professionals need to understand and use inferential statistics to interpret sample data from their organizations. Since in quality improvement and healthcare research studies all the data from a population often are not available, investigators take samples and make inferences about that population using inferential statistics. This series of six articles will give readers an understanding of the concepts of inferential statistics as well as the specific tools for calculating confidence intervals and tests of statistical significance for samples of data. This article, Part 6, merges the four concepts of the (1) standard error of the difference between sample means, (2) the z test statistic, (3) rejecting the null hypothesis, and (4) the p value to provide a comprehensive view of tests of statistical significance. This is followed by a description of t tests, statistical tests for comparing two sample proportions, and Type I and Type II errors. The series of articles concludes with a description of statistical significance versus meaningful difference.