Confidence Intervals

A key goal in applied biostatistics is to make inferences about unknown population parameters based on sample statistics. There are two broad areas of statistical inference, estimation and hypothesis testing.

Estimation is the process of determining likely values for a population parameter (e.g., the true population mean or true population proportion) based on a single random sample from the source population. In practice, we select a sample from the source population and use sample statistics (e.g., the sample mean or sample proportion) as estimates of the unknown parameter. The sample should be representative of the population, ideally with participants selected at random from the population. In generating estimates, it is also important to quantify sampling variability. 

After completing this course, the learner will be able to:

• Define point estimate, standard error, confidence level and margin of error
• Compare and contrast standard error and margin of error
• Compute and interpret confidence intervals for means and proportions
• Differentiate independent and matched or paired samples
• Compute confidence intervals for the difference in means and proportions in independent samples and for the mean difference in matched or paired samples
• Identify the appropriate confidence interval formula for a specific application based on the type of outcome variable and number of samples

Developed by: Lisa Sullivan, PhD; Wayne LaMorte, MD, PhD, MPH