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#DO A T TEST EXCEL FOR MAC HOW TO#

#DO A T TEST EXCEL FOR MAC HOW TO#

In our enhanced independent 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 an independent 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 independent t-test, reducing the validity of your results. Outliers are simply single 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 #4: There should be no significant outliers.

If you are unsure whether your study meets this assumption, you can use our Statistical Test Selector, which is part of our enhanced content. If your study fails this assumption, you will need to use another statistical test instead of the independent t-test (e.g., a paired-samples t-test). This is more of a study design issue than something you can test for, but it is an important assumption of the independent t-test. For example, there must be different participants in each group with no participant being in more than one group.

Assumption #3: You should have independence of observations, which means that there is no relationship between the observations in each group or between the groups themselves.

Example independent variables that meet this criterion include gender (2 groups: male or female), employment status (2 groups: employed or unemployed), smoker (2 groups: yes or no), and so forth.

Assumption #2: Your independent variable should consist of two categorical, independent groups.

You can learn more about continuous 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 on a continuous scale (i.e., it is measured at the interval or ratio level).

First, let's take a look at these six 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 an independent t-test when everything goes well! However, don't worry.

In practice, checking for these six 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 six 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 an independent t-test if your data "passes" six assumptions that are required for an independent t-test to give you a valid result. When you choose to analyse your data using an independent t-test, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using an independent 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 an independent t-test to give you a valid result.

This "quick start" guide shows you how to carry out an independent t-test using SPSS Statistics, as well as interpret and report the results from this test. Alternately, you could use an independent t-test to understand whether there is a difference in test anxiety based on educational level (i.e., your dependent variable would be "test anxiety" and your independent variable would be "educational level", which has two groups: "undergraduates" and "postgraduates"). For example, you could use an independent t-test to understand whether first year graduate salaries differed based on gender (i.e., your dependent variable would be "first year graduate salaries" and your independent variable would be "gender", which has two groups: "male" and "female"). The independent-samples t-test (or independent t-test, for short) compares the means between two unrelated groups on the same continuous, dependent variable. Independent t-test using SPSS Statistics Introduction