In our enhanced one-sample t-test guide, we: (a) show you how to detect outliers using SPSS Statistics and (b) discuss some of the options you have in order to deal with outliers. Fortunately, when using SPSS Statistics to run a one-sample t-test on your data, you can easily detect possible outliers. The problem with outliers is that they can have a negative effect on the one-sample t-test, reducing the accuracy of your results. Outliers are data points within your data that do not follow the usual pattern (e.g., in a study of 100 students' IQ scores, where the mean score was 108 with only a small variation between students, one student had a score of 156, which is very unusual, and may even put her in the top 1% of IQ scores globally). Assumption #3: There should be no significant outliers.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. Assumption #2: The data are independent (i.e., not correlated/related), which means that there is no relationship between the observations.You can learn more about interval and ratio variables in our article: Types of Variable. Examples of variables that meet this criterion include revision time (measured in hours), intelligence (measured using IQ score), exam performance (measured from 0 to 100), weight (measured in kg), and so forth. Assumption #1: Your dependent variable should be measured at the interval or ratio level (i.e., continuous).First, let’s take a look at these four assumptions: Even when your data fails certain assumptions, there is often a solution to overcome this. This is not uncommon when working with real-world data rather than textbook examples, which often only show you how to carry out a one-sample t-test when everything goes well! However, don’t worry. In practice, checking for these four assumptions just adds a little bit more time to your analysis, requiring you to click a few more buttons in SPSS Statistics when performing your analysis, as well as think a little bit more about your data, but it is not a difficult task.īefore we introduce you to these four assumptions, do not be surprised if, when analysing your own data using SPSS Statistics, one or more of these assumptions is violated (i.e., is not met). You need to do this because it is only appropriate to use a one-sample t-test if your data "passes" four assumptions that are required for a one-sample t-test to give you a valid result.
When you choose to analyse your data using a one-sample t-test, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using a one-sample t-test. 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. This "quick start" guide shows you how to carry out a one-sample t-test using SPSS Statistics, as well as interpret and report the results from this test.
You sample 1000 doctors in A & E departments and see if their hours differ from 100 hours. Alternately, you believe that doctors that work in Accident and Emergency (A & E) departments work 100 hour per week despite the dangers (e.g., tiredness) of working such long hours. Your sample would be pupils who received the new teaching method and your population mean would be the national average score. For example, you want to show that a new teaching method for pupils struggling to learn English grammar can improve their grammar skills to the national average. This population mean is not always known, but is sometimes hypothesized. The one-sample t-test is used to determine whether a sample comes from a population with a specific mean. One-Sample T-Test using SPSS Statistics Introduction
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