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"). Identify the appropriate confidence interval formula based on type of outcome variable and number of samples.Compute confidence intervals for the difference in means and proportions in independent samples and for the mean difference in paired samples.Differentiate independent and matched or paired samples.Compute and interpret confidence intervals for means and proportions.
Compare and contrast standard error and margin of error.Define point estimate, standard error, confidence level and margin of error.In generating estimates, it is also important to quantify the precision of estimates from different samples.Īfter completing this module, the student will be able to: The sample should be representative of the population, with participants selected at random from the population. In practice, we select a sample from the target population and use sample statistics (e.g., the sample mean or sample proportion) as estimates of the unknown parameter.
Estimation is the process of determining a likely value for a population parameter (e.g., the true population mean or population proportion) based on a random sample. There are two broad areas of statistical inference, estimation and hypothesis testing. Boston University School of Public HealthĪs noted in earlier modules a key goal in applied biostatistics is to make inferences about unknown population parameters based on sample statistics.
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