The observed interval may over- or underestimate μ. In practice, however, we select one random sample and generate one confidence interval, which may or may not contain the true mean. Strictly speaking a 95% confidence interval means that if we were to take 100 different samples and compute a 95% confidence interval for each sample, then approximately 95 of the 100 confidence intervals will contain the true mean value ( μ).
For both continuous variables (e.g., population mean) and dichotomous variables (e.g., population proportion) one first computes the point estimate from a sample. There are two types of estimates for each population parameter: the point estimate and confidence interval (CI) estimate. Proportion or rate, e.g., prevalence, cumulative incidence, incidence rateĭifference in proportions or rates, e.g., risk difference, rate difference, risk ratio, odds ratio, attributable proportion The table below summarizes parameters that may be important to estimate in health-related studies. Moreover, when two groups are being compared, it is important to establish whether the groups are independent (e.g., men versus women) or dependent (i.e., matched or paired, such as a before and after comparison).
The parameters to be estimated depend not only on whether the endpoint is continuous or dichotomous, but also on the number of groups being studied. Many of the outcomes we are interested in estimating are either continuous or dichotomous variables, although there are other types which are discussed in a later module. There are a number of population parameters of potential interest when one is estimating health outcomes (or "endpoints").