Do you need a normal distribution for a t-test?

Do you need a normal distribution for a t-test?

A t-test is a statistic method used to determine if there is a significant difference between the means of two groups based on a sample of data. Among these assumptions, the data must be randomly sampled from the population of interest and the data variables must follow a normal distribution.

Which type of tests require that data is normally distributed?

In statistics, normality tests are used to determine if a data set is well-modeled by a normal distribution and to compute how likely it is for a random variable underlying the data set to be normally distributed.

When to use independent samples in a t test?

You should use an Independent Samples T-Test in the following scenario: 1 You want to know if two groups are different on your variable of interest 2 Your variable of interest is continuous 3 You have two and only two groups 4 You have independent samples 5 You have a normal variable of interest

What are the requirements for an independent sample?

Your variable of interest should be continuous, be normally distributed, and have a similar spread between your 2 groups. Your 2 groups should be independent (not related to each other) and you should have enough data (more than 5 values in each group).

What is the assumption of normality in the independent t test?

The test (dependent) variable is normally distributed within each of the two populations (as defined by the grouping variable). This is commonly referred to as the assumption of normality. The variances of the test (dependent) variable in the two populations are equal. This is commonly referred to as the assumption of homogeneity of variance.

When to use Levene’s test or independent samples t test?

SPSS conveniently includes a test for the homogeneity of variance, called Levene’s Test, whenever you run an independent samples t test. This implies that if we reject the null hypothesis of Levene’s Test, it suggests that the variances of the two groups are not equal; i.e., that the homogeneity of variances assumption is violated.