The standard deviation (SD) of both averagesģ. The average of both sample (observed averages)Ģ. Alternative hypothesis - the difference between the observed mean of birthweight for program babies and the expected mean of birthweight for poor women is not zero.Ĭalculation of the test statistic requires three components: 1.Another way of stating the null hypothesis is that the difference between the mean of the treatment group of birthweight for program babies and the mean of the control group of birthweight for poor women is zero. Null hypothesis is that the difference between the two groups is 0.Specifically, we want to establish a null hypothesis and an alternative hypothesis to be evaluated with data. The first step to examining this question is to establish the specific hypotheses we wish toĮxamine. Returning to the two-sample t-test, the steps to conduct the test are similar to those of the one-sample test. In this example, mothers who are part of the prenatal care program to reduce the likelihood of low birthweight is the treatment group, with a control group comprised of women who do not take part in the program. The group receiving the treatment to be evaluated is referred to as the treatment group, while those who do not are referred to as the control or comparison group. To evaluate the effects of some intervention, program, or treatment, a group of subjects is divided into two groups. In this example, rather than comparing the birthweight of a group of infant to some national average, we will examine a program's effect by comparing the birthweights of babies born to women who participated in an intervention with the birthweights of a group that did not.Ī comparison of this sort is very common in medicine and social science.
And for the sake of consistency, we will focus on another example dealing with birthweight and prenatal care. The steps of conducting a two-sample t-test are quite similar to those of the one-sample test. The two-sample t-test is a hypothesis test for answering questions about the mean where the data are collected from two random samples of independent observations, each from an underlying normal distribution: For example, there are many questions in which we want to compare two categories of some categorical variable (e.g., compare males and females) or two populations receiving different treatments in context of an experiment.
We often want to know whether the means of two populations on some outcome differ.
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