Section 3
Comparing More than Two Means
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By Boundless
Boundless Statistics
Statistics
by Boundless
5 concepts
Elements of a Designed Study
The problem of comparing more than two means results from the increase in Type I error that occurs when statistical tests are used repeatedly.
Randomized Design: Single-Factor
Completely randomized designs study the effects of one primary factor without the need to take other nuisance variables into account.
![Thumbnail](../../../../../../figures.boundless-cdn.com/18374/square/-06-30-20at-201.38.49-20pm.jpeg)
Multiple Comparisons of Means
ANOVA is useful in the multiple comparisons of means due to its reduction in the Type I error rate.
![Thumbnail](../../../../../../figures.boundless-cdn.com/18245/square/d-observational-studies-06.gif)
Randomized Block Design
Block design is the arranging of experimental units into groups (blocks) that are similar to one another, to control for certain factors.
![Thumbnail](../../../../../../figures.boundless-cdn.com/18375/raw/factorial-design.jpg)
Factorial Experiments: Two Factors
A full factorial experiment is an experiment whose design consists of two or more factors with discrete possible levels.