Data Science Course > Analysis of Variance

Summary of ANOVA in Data Science

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The one-way ANOVA (Analysis of Variance) is a parametric test for determining if three or more groups have statistically significant differences in outcomes. ANOVA looks for a general difference, meaning that at least one of the groups is statistically distinct from the rest.

The analysis of variance (ANOVA) is a statistical technique for determining if the means of two or more groups differ significantly. ANOVA compares the means of different samples to determine the impact of one or more factors.

ANOVA, like the t-test, can be used to determine whether differences in data groups are statistically significant. It analyses the levels of variance within the groups using samples from each one.

We use a t-test to find the mean between two samples/groups when there are only two, but it is not as reliable when there are more than two, therefore we use ANOVA.

Some other uses include:

  • Testing for normality
  • Testing for equality of variances between groups
  • Testing for equality of regression slopes between groups
  • Testing whether there are significant differences in means across groups

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