For example, if you're investigating differences between men and women in the mean education level, your null hypothesis will usually be that they are the same. Usually the null hypothesis is the opposite of what you're really interested in. This is called a two-sample t-test, and is the most common.ДЇor all these tests we've described the null hypothesis. The final type of hypothesis we'll consider is whether two groups have the same population mean for a single variable. The Population Means for Two Subsamples are the Same the value of X for observation 1 has a relationship to the value of Y for observation 1 that does not exist between the value of X for observation 1 and the value of Y for observation 2). This is called a paired-sample t-test, because the test assumes that the values of the two variables for the same observation go together (i.e. The Population Means for Two Variables are the SameДЄnother type of hypothesis looks at whether two variables have the same population mean. This is called a single-sample t-test, because you look at the entire sample at once. One type of hypothesis simply asks whether the population mean of a variable is equal to some particular value of interest. In this section we'll discuss the following types of tests: The Population Mean is Equal to Some Specified Value The command to run one is simply ttest, but the syntax will depend on the hypothesis you want to test.

T-tests are frequently used to test hypotheses about the population mean of a variable. If you are new to Stata we strongly recommend reading all the articles in the Stata Basics section. If you prefer, you can request that Stata run a Welch approximation, which can be useful in small sample-size situations (see here for a brief explanation of Welch's approximation).This article is part of the Stata for Students series. Thus the whole command would look something like ttest intrinsi, by(gender) unequal Doing so will cause Stata to run a Satterthwaite approximation on the data (calculating the t-statistic without equal variances) To see a nicely detailed example of the Satterthwaite, see here. If these tests come back with a significant result (meaning the variances are not equal) you can simply add unequal to the t-test-command. To run this command type robvar variable, by(sorting variable) The output will yield the following result: It will report the initial result as well as the same test performed with a median replacement and a 10% trimmed mean replacement, based on the thoughts of two statisticians. In addition to the sdtest, Stata will perform Levene's test of equal variances. Similar to the t-test output, this test yields the following result: To see whether the standard deviations are approximately equal, you can use the built-in Stata command, typing sdtest variable, by(sortingvariable)В . In addition to the basic t-test, Stata has options for cases where the variances aren't approximately equal (an assumption of the independent-test).

In the example below, we want to test if a made up sample of college students' hours spent on homework each night significantly differs by class year (freshman vs.

The output from this command is fairly simple. Once these are available, type ttest, by() the independent variable) and one continuous, discrete, normally distributed variable (the dependent variable). To run an independent means t-test in Stata requires one grouping variable (a.k.a. The dependent variable is the score (for example, level of intrinsic motivation) and the independent variable is whatever differentiates the two scores (for example, gender). The null hypothesis is that the two groups do not differ significantly from one another. An independent means t-test compares two sets of scores from two different groups of people.

T test stata