![]() The output provides the following details about the two-sample T-test: This allows you to select the confidence level (optional).Įnter the hypothesized difference value as mentioned in the question or preset by theĪt this point it lets one decide if a one tailed test or two tailed tests is required.įurther, it allows you to assume equal variances by checking the last option.įinally, click on ok and ok, to get the final output. Select the option “each sample is in its column”įollowing options are available in this window: Then click on the Stat tab on the top panel of Minitab.įrom the drop-down select basic statistics.įrom the sub-dropdown menu select “2-sample t”.įollowing window of two-sample t-test shows up The following image shows where can the two-sample t-test be located on the Minitab.Įnter the data of two samples in columns C1 and C2. 2 sample t test minitab software#Minitab is a statistical software that helps through various statistical operations by using the correct functions. ![]() It is based on the value of degree of freedom, level of significance and the knowledge of whether the test is one-tail or two tailed. The t critical value can be obtained from the t table. Here, S 1 S_1 S 1 and S 2 S_2 S 2 are sample standard deviations of first and second sample respectively. Here, n 1 n_1 n 1 and n 2 n_2 n 2 are samples sizes of the first and second samples, respectively. The formula of df is as follows:ĭ f = n 1 + n 2 − 2 Df = t = n 1 s 1 2 + n 2 s 2 2 x 1 − x 2 The t critical value or p value are defined by the degree of freedom. In a t-test there is a concept of degrees of freedom. Two sample t-test (equal variances): When the question relates to comparing mean values of two different groups and it is particularly specified to assume equal population variances, in that scenario the pooled t-test is used. Two sample t-tests (unequal variances): Generally, when a question relates to comparing mean values of two different groups, by default it is assumed to follow the working of unequal variances. Both the tests are related to two independent samples. Got a question on this topic?ĭepending on the type of test or the variables involved or the exact claim that the researcher wants to test, there are two different types of two-sample t-tests. If there no information about the equality of the population variance is given, then apply the two independent sample t test with unequal variances. In the case of independent samples where the variances are assumed to be equal, a two-sample pooled t-test is used. If the samples are not related to each other, check if the variances are assumed to be equal or not. ![]() ![]() ![]() If the samples are related to each other a paired t-test is used. In the two-sample scenario, there can be: samples that are related to each other and there can be samples that are not related to each other. It helps to compare the two population means under study and test the beliefs and claims of the researcher about the equality of the population means. All these different tests are applicable in different scenarios and tend to evaluate different types of claims and hypotheses.Ī t-test is the most used inferential statistical tool. Some tests relate to Z statistic, t statistic, F statistic, and Chi-square. These tests are based on several different statistics. Hypothesis testing is an important tool in inferential statistics. ![]()
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