![]() Here we consider hypothesis testing with a discrete outcome variable in a single population. Identify the appropriate hypothesis testing procedure based on type of outcome variable and number of samples.Appropriately interpret results of chi-square tests.Learning ObjectivesĪfter completing this module, the student will be able to: We will consider chi-square tests here with one, two and more than two independent comparison groups. Specifically, the test statistic follows a chi-square probability distribution. The technique to analyze a discrete outcome uses what is called a chi-square test. ![]() ![]() We could use the same classification in an observational study such as the Framingham Heart Study to compare men and women in terms of their blood pressure status - again using the classification of hypertensive, pre-hypertensive or normotensive status. For example, in some clinical trials the outcome is a classification such as hypertensive, pre-hypertensive or normotensive. The specific tests considered here are called chi-square tests and are appropriate when the outcome is discrete (dichotomous, ordinal or categorical). The hypothesis is based on available information and the investigator's belief about the population parameters. This module will continue the discussion of hypothesis testing, where a specific statement or hypothesis is generated about a population parameter, and sample statistics are used to assess the likelihood that the hypothesis is true. Boston University School of Public Health
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