CAN'T MISS--conquer any number task by making important statistics simple. Part 3. Standard error, estimation, and confidence intervals.
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 3, describes standard error and margin of error for a continuous variable and how they are calculated from the sample size and standard deviation of a sample. The article then demonstrates how the standard error and margin of error are used to calculate the confidence interval for estimating a population mean based on a sample mean.
Hansen
John P
JP
Group Health Cooperative of South Central, Wisconsin, Madison, WI, USA. john_hansen@ghc-hmo.com
D000704:Analysis of Variance; D001835:Body Weight; D016001:Confidence Intervals; D003626:Data Display; D003627:Data Interpretation, Statistical; D003710:Demography; D006302:Health Services Research / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D016011:Normal Distribution; D011336:Probability; D018401:Sample Size; D012494:Sampling Studies; D017749:Total Quality Management / Q000706:statistics & numerical data*