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Introduction to Biostatistics, Harvard Extension School, Fall, 2005 © Scott Evans, Ph.D.1 Analysis of Variance (ANOVA)

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Presentation on theme: "Introduction to Biostatistics, Harvard Extension School, Fall, 2005 © Scott Evans, Ph.D.1 Analysis of Variance (ANOVA)"— Presentation transcript:

1 Introduction to Biostatistics, Harvard Extension School, Fall, 2005 © Scott Evans, Ph.D.1 Analysis of Variance (ANOVA)

2 Introduction to Biostatistics, Harvard Extension School, Fall, 2005 © Scott Evans, Ph.D.2 Recall  2-sample t-test  H 0 :  1 =  2  H A :  1   2

3 Introduction to Biostatistics, Harvard Extension School, Fall, 2005 © Scott Evans, Ph.D.3  However, what do we do if there are more than 2 groups to compare?  We could conduct all possible pairwise comparisons.  But this is inefficient (particularly if the number of groups is large)  Multiple testing issue (inflated Type I error)

4 Introduction to Biostatistics, Harvard Extension School, Fall, 2005 © Scott Evans, Ph.D.4 ANOVA  ANOVA is a methodology that provides a opportunity to compare the means of 2 or more groups.  It is more efficient than all possible pairwise comparisons  It controls for the multiple testing issue.  Note: when the number of groups is 2, then ANOVA simplifies to the 2-sample t-test.  Similar to the t-test, ANOVA is used when the outcome variable of interest is continuous.

5 Introduction to Biostatistics, Harvard Extension School, Fall, 2005 © Scott Evans, Ph.D.5 ANOVA  Hypotheses:  H 0 : Population means of ALL groups are equal  H A : At least one population mean is not equal to the others.

6 Introduction to Biostatistics, Harvard Extension School, Fall, 2005 © Scott Evans, Ph.D.6 ANOVA  ANOVA attempts to answer questions about group means, however it does so by “analysis of variance”.  The key idea is that total variability may be divided into: (1) variability within groups, and (2) variability between groups.  If there is no difference between group means then these 2 sources of variability should be similar.  Thus by comparing these sources of variability, we can compare the group means.

7 Introduction to Biostatistics, Harvard Extension School, Fall, 2005 © Scott Evans, Ph.D.7 ANOVA  Has several assumptions that must be checked.  Utilizes an F-test with a numerator and denominator degrees of freedom (Table A.5 in the text)  We obtain an ANOVA Table which summarizes the sources of variation, degrees of freedom, and F-test results (p- value, etc.)

8 Introduction to Biostatistics, Harvard Extension School, Fall, 2005 © Scott Evans, Ph.D.8 ANOVA  If the null hypothesis (that all of the group means are equal) is not rejected, then in general there is no need to investigate further.  One may still investigate power and potential Type II error.  However, if the null hypothesis is rejected, then sufficient evidence has been found to conclude that at least one of the means (and possibly more) is different from the others.

9 Introduction to Biostatistics, Harvard Extension School, Fall, 2005 © Scott Evans, Ph.D.9 ANOVA  Where are the differences?  Which means are different?  We answer these questions using post-hoc tests.

10 Introduction to Biostatistics, Harvard Extension School, Fall, 2005 © Scott Evans, Ph.D.10 ANOVA post-hoc tests  Overall experiment-wise error rate can be controlled regardless of the number of pairwise comparisons.  Examples:  Bonferroni  Tukey  Scheffe  Others

11 Introduction to Biostatistics, Harvard Extension School, Fall, 2005 © Scott Evans, Ph.D.11 Nonparametric Analog  If the normality assumptions do not hold then nonparametric methods can be applied.  Kruskal-Wallis one-way ANOVA  I use this a lot in practice

12 Introduction to Biostatistics, Harvard Extension School, Fall, 2005 © Scott Evans, Ph.D.12 ANOVA  Insert ANOVA.pdf

13 Introduction to Biostatistics, Harvard Extension School, Fall, 2005 © Scott Evans, Ph.D.13 ANOVA Example  Insert ANOVA_example.pdf

14 Introduction to Biostatistics, Harvard Extension School, Fall, 2005 © Scott Evans, Ph.D.14 Extensions  If there is more than one factor (for example a blocking factor), then one may elect a two-way (or higher level) ANOVA. One may also look at interactions between factors.  Nonparametric Analog  Friedman two-way ANOVA


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