title: CAN'T MISS--conquer any number task by making important statistics simple. Part 2. Probability, populations, samples, and normal distributions.
abstract: Healthcare quality improvement professionals need to understand and use inferential statistics to interpret sample data from their organizations. In quality improvement and healthcare research studies all the data from a population often are not available, so investigators take samples and make inferences about the population by using inferential statistics. This three-part series will give readers an understanding of the concepts of inferential statistics as well as the specific tools for calculating confidence intervals for samples of data. This article, Part 2, describes probability, populations, and samples. The uses of descriptive and inferential statistics are outlined. The article also discusses the properties and probability of normal distributions, including the standard normal distribution.
authors: Group Health Cooperative of South Central, Wisconsin, Madison, WI, USA. john_hansen@ghc-hmo.com
authors: John P
authors: JP
authors: Hansen
mesh_terms: D003627:Data Interpretation, Statistical; D003710:Demography; D003920:Diabetes Mellitus / Q000097:blood; D006442:Glycated Hemoglobin / Q000032:analysis; D006302:Health Services Research / Q000706:statistics & numerical data*; D006801:Humans; D016011:Normal Distribution; D011336:Probability; D012494:Sampling Studies; D017749:Total Quality Management / Q000706:statistics & numerical data*
chemical_list: D006442:Glycated Hemoglobin