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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