When we execute in the Minitab we would get the values like this: Then, next to “Alternative hypothesis,” select “Difference postulated difference” from the drop-down box. We write 0 next to “Hypothesized difference:” because we’re checking whether the difference is statistically significant. Select “Options…” from the drop-down menu. Next, enter a value under C1 and C2 for Asian and Nippon rate to check for a normal distribution.Įnter the confidence level value in the tab and click ok. Select Each sample in the same column for a pair-t. Under C1, put the “After Asianet” data, and under C2, insert the “Nippon rate” data. The relevance is 0.05, while the confidence interval is 95 percent (1 – 0.05 = 0.95). ii) The information is presented in the table below.And the final result is shown here calculating the difference and mean as well. Select 2- sample test from stat in the menu bar and give confidence level values and click ok. The 2-sample T-test compares two classes within the same categorical variable, which itself is useful when attempting to answer concerns about the effects of adding a program or making a modification to a group of participants.Įntering a two-sample test in C1 and C2 for two purchase dates p1 and p2 as shown in the previous example. If the P-value exceeds the significance level, the null hypothesis is not rejected. If the P-value is less than the significance level, the null hypothesis must be rejected. Mean hike grade (12, 95% CI, 6.67 to 17.1) was lower than the normal hike grade of 4.0, a statistically significant difference, t(3.2) = -2.83, p =. Minitab’s output is presented in the image below.Ī one-sample t-test was run to determine whether employees’ Hike grade was different to high, defined as a score of 3.0. Minitab’s default confidence intervals are 95 percent, which amounts to reporting statistically significant at the p<.05 level. Finally, we’ll get the dialogue box displayed below: Select Stat- > and navigate to Basic Statistics > 1-Sample t… on the top menu, as shown below:Įnd up leaving the Samples in Columns option checked and type Hike Grade into the space below. As a result, below are the three steps needed to execute a one-sample t-test in Minitab: When the four assumptions in the preceding part have still not been violated, this step illustrates how to evaluate the data in Minitab using a one-sample t-test. The grade on the dependent variable was then input. The dependent variable, Hike Grade, is put up in Minitab under column C1. As a result, all 30 participants’ scores are calculated, and a one-sample t-test is employed to see if this sample is typical of the general population. Let’s say 30 of the participants had grade scores that are labeled ‘Hike.’ Assume that a score of 3.0 relates to the word ‘Hike.’ The lower the score, there is no hike, and the higher the score, the more likely people are to hike.
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