![]() 2 Stichler, R.D., Richey, G.G., Mandel, J. Note that the p-value is the probability of incorrectly rejecting the null hypothesis (i.e. This is because the p-value for the A-D test is 0.887, which is greater than 0.10, a frequently used level of significance for such a hypothesis test (as opposed to the more traditional 0.05 level). H1: data do not follow a Normal distribution, at the α = 0.10 significance level. The Anderson-Darling (A-D) Normality Test illustrated in Figure 1 shows that we are unable to reject the null hypothesis, H0: data follow a Normal distribution vs. We may therefore wish to create a column for the differences between the two measurement systems, and investigate the distributional properties. An assumption for the paired t-test procedure is that the distribution in which the differences analyzed come from is Normal (a.k.a. Each tire was subject to measurement by two different methods, the first based on weight loss, and the second based on groove wear. For this example we shall consider a set of data from Stichler, Richey and Mandel 2 that deal with measurements of tire wear (thous mi). Gosset published under the pseudonym “Student” 1 which is why it is frequently referred to as “Student’s” t-test. The t-test was originally developed by the English statistician William Sealy Gosset (1876- 1937), whilst working for a brewing company in Ireland. test H0 : μdifference = 0, versus an alternative hypothesis such as H1 : μdifference ≠ 0. We may wish to test if the mean difference is significantly different from zero, i.e. What follows is an example of the paired (or dependent) t-test procedure using the popular statistical software package, MINITAB ®. For example, if one were to test pulse rates for a group of individuals prior to, then upon completion of, a fitness regime, it would be appropriate to compare the pulse rates for each individual. The paired t-test procedure is used to compare the mean difference between two populations when one believes that some dependency exists. Then, click OK to return to the main pop-up window.The Paired T-Test Using MINITAB By Keith M. In the pop-up window that appears, for the box labeled Alternative, select either less than, greater than, or not equal depending on the direction of the alternative hypothesis: (For Welch's t-test, leave the box labeled Assume equal variances unchecked.):Ĭlick on the button labeled Options. For the two-sample (pooled) t-test, click on the box labeled Assume equal variances. Specify the name of the Samples variable (Prey, for us) and specify the name of the Subscripts (grouping) variable (Group, for us). In the pop-up window that appears, select Samples in one column. Under the Stat menu, select Basic Statistics, and then select 2-Sample t.: Let's use the data and Minitab to test whether the mean prey size of the populations of the two types of spiders differs.Įnter the data in one column (called Prey, say), and the grouping variable in a second column (called Group, say, with 1 denoting a deinopis spider and 2 denoting a menneus spider), such as: Size of Random Pray Samples of the Menneus Spider in Millimeters sample 1 The following data were obtained on the size, in millimeters, of the prey of random samples of the two species: Size of Random Pray Samples of the Deinopis Spider in Millimeters sample 1 The species, the deinopis, and menneus coexist in eastern Australia. Let's recall the spider and prey example, in which the feeding habits of two species of net-casting spiders were studied. Just as is the case for asking Minitab to calculate pooled t-intervals and Welch's t-intervals for \(\mu_1-\mu_2\), the commands necessary for asking Minitab to perform a two-sample t-test or a Welch's t-test depend on whether the data are entered in two columns, or the data are entered in one column with a grouping variable in a second column.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |