Journal for healthcare quality : official publication of the National Association for Healthcare Quality
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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. ⋯ A histogram is a graph that displays the frequency distribution for a continuous variable. The article also demonstrates how to calculate the mean, median, standard deviation, and variance for a continuous variable.
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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 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.
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Healthcare quality improvement (QI) studies are systematic analyses of processes and outcomes. As such, they meet one of the federal regulatory criteria defining research. ⋯ Therefore, to err on the side of patients' rights, any studies involving human subjects or human data must be approved by federally mandated institutional review boards (IRBs) or their designees. This article explores federal regulations to protect research subjects, levels of IRB review, and strategies to simplify the IRB approval process and discusses implications for QI studies.