Contents
What assumptions should be tested?
Typical assumptions are:
- Normality: Data have a normal distribution (or at least is symmetric)
- Homogeneity of variances: Data from multiple groups have the same variance.
- Linearity: Data have a linear relationship.
- Independence: Data are independent.
What are the three assumptions in statistics?
A few of the most common assumptions in statistics are normality, linearity, and equality of variance.
What are assumptions made when conducting a t-test?
T-Test Assumptions. The first assumption made regarding t-tests concerns the scale of measurement. The assumption for a t-test is that the scale of measurement applied to the data collected follows a continuous or ordinal scale, such as the scores for an IQ test. The second assumption made is that of a simple random sample,…
When to use a t test in statistics?
The T-Test. A t-test is an analysis of two populations means through the use of statistical examination; a t-test with two samples is commonly used with small sample sizes, testing the difference between the samples when the variances of two normal distributions are not known. T-distribution is basically any continuous probability distribution…
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.
Is it possible to keep outliers in a t test?
It’s also possible to keep the outliers in the data and perform Wilcoxon test or robust t-test using the WRS2 package. The normality assumption can be checked by computing the Shapiro-Wilk test for each group.