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An analysis of variance (ANOVA) evaluates the different groups in a study to determine if there are differences between the groups. The results tell us if there is a difference noted either between groups or between several intervals on one group. Unlike the t-test, the analysis of variance can be used on more than two groups.

However, in order to utilize the ANOVA, the groups must meet the following criteria:

The samples are normally distributed with equal variance, the groups are mutually exclusive (are different from one another), the dependent variables are measured on an interval or ratio scale, and all observations in each group are independent (not related to each other)

Example: If we want to compare the effects of blood pressure reducing methods on a group of patients.

We can divide a group of 40 into four groups. Group 1 takes furosemide only; Group 2 takes furosemide and meditates; Group 3 meditates; Group 4 is the control and has no intervention.

Using ANOVA we can evaluate all four groups at once. This is not possible with the t-test, nor would we want to use it as we would need to do multiple calculations increasing our risk of incorrectly rejecting the null hypothesis.


Grove, S.K., Cipher, D. (2017). Statistics for nursing research: A workbook for evidence-based

practice (2nd ed) (pp. 377-378). St. Louis, MO: Elsevier. Saunders, 022016. VitalBook file Retrieved from:!/4/8%5BcaretDiv%5D%400:268

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