- Normality test minitab how to#
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Suppose that a line manager is seeking to assess how consistently a production line is producing. If the data is symmetrically distributed and most results are located in the middle, we can say that the data is normally distributed. This will give you an insight about the shape of the distribution and whether it is normal or not. It is always a good practice to plot your data in a histogram after collecting the data. Histograms are efficient graphical methods for describing the distribution of data. You can test for normality using the Shapiro-Wilk test of normality, which is easily tested for using Minitab. We talk about the one-sample t-test only requiring approximately normal data because it is quite “robust” to violations of normality, meaning that the assumption can be a little violated and still provide valid results.
Assumption #4: Your dependent variable should be approximately normally distributed. Fortunately, when using Minitab to run a one-sample t-test on your data, you can easily detect possible outliers. Where a score is an outlier this is problematic because outliers can have a disproportionately negative effect on the one-sample t-test, reducing the accuracy of its results. As such, a student scoring just 2 on the scale “could” be considered an outlier. However, one student scores just 2 on the scale, with the second lowest test anxiety score being 36. The mean test anxiety score was 56 and the vast majority of students scored between 42 and 70. For example, consider a study examining the test anxiety of 500 students where anxiety was measured on a scale of 0-100, with 0 = no anxiety and 100 = maximum anxiety. An outlier is simply a case within your data set that does not follow the usual pattern. Assumption #3: There should be no significant outliers. Assumptions #3 and #4 are explained below: However, there are possible solutions to correct such violations (e.g., transforming your data) such that you can still use a one-sample t-test. In fact, do not be surprised if your data violates one or more of these assumptions. You have to check that your data meets these assumptions because if it does not, the results you get when running a one-sample t-test might not be valid. This is more of a study design issue than something you can test for, but it is an important assumption of the one-sample t-test.Īssumptions #3 and #4 relate to the nature of your data and can be checked using Minitab. Assumption #2: The data are independent (i.e., not correlated/related), which means that there is no relationship between the observations. If you are unsure whether your dependent variable is continuous (i.e., measured at the interval or ratio level), see our Types of Variable guide. Examples of continuous variables include height (measured in feet and inches), temperature (measured in ☌), salary (measured in US dollars), revision time (measured in hours), intelligence (measured using IQ score), firm size (measured in terms of the number of employees), age (measured in years), reaction time (measured in milliseconds), grip strength (measured in kg), power output (measured in watts), test performance (measured from 0 to 100), sales (measured in number of transactions per month), academic achievement (measured in terms of GMAT score), and so forth. Assumption #1: Your dependent variable should be measured at a continuous level (i.e., it is an interval or ratio variable). Assumptions #1 and #2 are explained below: If these assumptions are not met, there is likely to be a different statistical test that you can use instead. However, you should check whether your study meets these two assumptions before moving on. You cannot test the first two of these assumptions with Minitab because they relate to your study design and choice of variables. AssumptionsĪ one-sample t-test has four assumptions. 
However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a one-sample t-test to give you a valid result.
Normality test minitab how to#
In this guide, we show you how to carry out a one-sample t-test using Minitab, as well as interpret and report the results from this test. Advanced Digital Marketing Masters Program.
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