Decoding the Data: Confidence Intervals
Scott Sherrill-Mix, Ph.D., explains confidence intervals as a statistical tool used to quantify uncertainty in estimates derived from sample data using a real figure example.
Video Notes
Sherrill-Mix explains a confidence interval using a study on blood culture contamination as an example. He explains what they are, how to interpret them correctly and common misunderstandings.A confidence interval quantifies the uncertainty in an estimate depending on variability in data and sample size and is designed so that, over many repeated studies, 95% of such intervals would contain the true (fixed) population value.
Sherrill-Mix emphasizes common misconceptions, clarifying that a 95% confidence interval does not mean there is a 95% probability the true value lies within a specific interval, nor that future observations will fall within it. Instead, the true value is fixed, and the interval itself varies across samples.
Watch this video to learn how larger samples produce narrower, more precise intervals, while small samples require the use of a t-distribution to avoid overconfidence.
Author Information
Scott Sherrill-Mix, Ph.D., Assistant Professor, Department of Microbiology, Genetics and Immunology, Michigan State University.Stay up to date on all ASM Clinical Public Health Microbiology (CPHM) activities when you sign up for our monthly CPHM Newsletter. Be the first to know about free educational webinars, continuing education credit opportunities, advocacy, updated guidelines and more.
