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Which is better one-way or two-way ANOVA?
1. A one-way ANOVA is primarily designed to enable the equality testing between three or more means. A two-way ANOVA is designed to assess the interrelationship of two independent variables on a dependent variable.
What would happen if instead of using an ANOVA to compare 10 groups you perform multiple t tests?
What would happen if instead of using an ANOVA to compare 10 groups, you performed multiple t- tests? Nothing, there is no difference between using an ANOVA and using a t-test.
What do you need to know about one way ANOVA?
A one-way ANOVA (“analysis of variance”) compares the means of three or more independent groups to determine if there is a statistically significant difference between the corresponding population means. The motivation for performing a one-way ANOVA. The assumptions that should be met to perform a one-way ANOVA.
Is the one-way ANOVA an extension of the independent two sample t test?
The one-way ANOVA is an extension of the independent two-sample t-test. In the above example, if we considered only two age groups, say below 40 and above 40, then the independent samples t-test would have been enough although application of ANOVA would have also produced the same result.
How does ANOVA control for Type I errors?
An ANOVA controls for these errors so that the Type I error remains at 5% and you can be more confident that any statistically significant result you find is not just running lots of tests. See our guide on hypothesis testing for more information on Type I errors.
How is null hypothesis tested in one way ANOVA?
In one-way ANOVA, the interest lies in testing the null hypothesis that the category means are equal in the population. Under the null hypothesis, SSx and SSerror come from the same source of variation. In such case, the estimate of the population variation of Y can be based on either between or within category variation of X.