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Does ANOVA require independent samples?
ANOVA, which stands for Analysis of Variance, is a statistical test used to analyze the difference between the means of more than two groups. A one-way ANOVA uses one independent variable, while a two-way ANOVA uses two independent variables.
Can ANOVA be used instead of t-test?
The Student’s t test is used to compare the means between two groups, whereas ANOVA is used to compare the means among three or more groups. In ANOVA, when using one categorical independent variable, it is called one-way ANOVA, whereas for two categorical independent variables, it is called two-way ANOVA.
When to use one way ANOVA or independent samples?
If you have three or more groups, you should use a One Way Anova analysis instead. If you only have one group and you would like to compare your group to a known or hypothesized population value, you should use a Single Sample T-Test instead. Independent samples means that your two groups are not related in any way.
What are the assumptions of the t test ANOVA?
The t-test ANOVA have three assumptions: independence assumption (the elements of one sample are not related to those of the other sample), normality assumption (samples are randomly drawn from the normally distributed populstions with unknown population means; otherwise the means are no longer best measures of central tendency,…
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 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.