You can run these tests using SPSS Statistics, the procedure for which can be found in our Testing for Normality guide. You can test for this using a number of different tests, but the Shapiro-Wilks test of normality or a graphical method, such as a Q-Q Plot, are very common. Note: Technically, it is the residuals that need to be normally distributed, but for an independent t-test, both will give you the same result. The independent t-test requires that the dependent variable is approximately normally distributed within each group. An example would be gender - an individual would have to be classified as either male or female – not both.Īssumption of normality of the dependent variable Often we are investigating differences in individuals, which means that when comparing two groups, an individual in one group cannot also be a member of the other group and vice versa. Unrelated groups, also called unpaired groups or independent groups, are groups in which the cases (e.g., participants) in each group are different. One independent, categorical variable that has two levels/groups.In order to run an independent t-test, you need the following: What do you need to run an independent t-test? Most commonly, this value is set at 0.05. To do this, we need to set a significance level (also called alpha) that allows us to either reject or accept the alternative hypothesis. In most cases, we are looking to see if we can show that we can reject the null hypothesis and accept the alternative hypothesis, which is that the population means are not equal: The null hypothesis for the independent t-test is that the population means from the two unrelated groups are equal:

Null and alternative hypotheses for the independent t-test The independent t-test, also called the two sample t-test, independent-samples t-test or student's t-test, is an inferential statistical test that determines whether there is a statistically significant difference between the means in two unrelated groups. Independent t-test for two samples Introduction

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