A one-way ANOVA is used to determine whether or not there is a statistically significant difference between the means of three or more independent groups.

A one-way ANOVA uses the following null and alternative hypotheses:

**H**: All group means are equal._{0}**H**: Not all group means are equal._{A}

If the overall p-value of the ANOVA is less than a certain significance level (e.g. α = .05) then we reject the null hypothesis and conclude that not all of the group means are equal.

In order to find out which group means are different, we can then perform **post-hoc pairwise comparisons**.

The following example shows how to perform the following post-hoc pairwise comparisons in R:

- The Tukey Method
- The Scheffe Method
- The Bonferroni Method
- The Holm Method

**Example: One-Way ANOVA in R**

Suppose a teacher wants to know whether or not three different studying techniques lead to different exam scores among students. To test this, she randomly assigns 10 students to use each studying technique and records their exam scores.

We can use the following code in R to perform a one-way ANOVA to test for differences in mean exam scores between the three groups:

#create data frame df <- data.frame(technique = rep(c("tech1", "tech2", "tech3"), each=10), score = c(76, 77, 77, 81, 82, 82, 83, 84, 85, 89, 81, 82, 83, 83, 83, 84, 87, 90, 92, 93, 77, 78, 79, 88, 89, 90, 91, 95, 95, 98)) #perform one-way ANOVA model <- aov(score ~ technique, data = df) #view output of ANOVA summary(model) Df Sum Sq Mean Sq F value Pr(>F) technique 2 211.5 105.73 3.415 0.0476 * Residuals 27 836.0 30.96 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

The overall p-value of the ANOVA (.0476) is less than α = .05 so we’ll reject the null hypothesis that the mean exam score is the same for each studying technique.

We can proceed to perform post-hoc pairwise comparisons to determine which groups have different means.

**The Tukey Method**

The Tukey post-hoc method is best to use when the sample size of each group is equal.

We can use the built-in **TukeyHSD()** function to perform the Tukey post-hoc method in R:

#perform the Tukey post-hoc method TukeyHSD(model, conf.level=.95) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = score ~ technique, data = df) $technique diff lwr upr p adj tech2-tech1 4.2 -1.9700112 10.370011 0.2281369 tech3-tech1 6.4 0.2299888 12.570011 0.0409017 tech3-tech2 2.2 -3.9700112 8.370011 0.6547756

From the output we can see that the only p-value (“**p adj**“) less than .05 is for the difference between technique and technique 3.

Thus, we would conclude that there is only a statistically significant difference in mean exam scores between students who used technique 1 and technique 3.

**The Scheffe Method**

The Scheffe method is the most conservative post-hoc pairwise comparison method and produces the widest confidence intervals when comparing group means.

We can use the** ScheffeTest()** function from the DescTools package to perform the Scheffe post-hoc method in R:

library(DescTools) #perform the Scheffe post-hoc method ScheffeTest(model) Posthoc multiple comparisons of means: Scheffe Test 95% family-wise confidence level $technique diff lwr.ci upr.ci pval tech2-tech1 4.2 -2.24527202 10.645272 0.2582 tech3-tech1 6.4 -0.04527202 12.845272 0.0519 . tech3-tech2 2.2 -4.24527202 8.645272 0.6803 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 156

From the output we can see that there are no p-values less than .05, so we would conclude that there is no statistically significant difference in mean exam scores among any groups.

**The Bonferroni Method**

The Bonferroni method is best to use when you have a set of planned pairwise comparisons you’d like to make.

We can use the following syntax in R to perform the Bonferroni post-hoc method:

#perform the Bonferroni post-hoc method pairwise.t.test(df$score, df$technique, p.adj='bonferroni') Pairwise comparisons using t tests with pooled SD data: df$score and df$technique tech1 tech2 tech2 0.309 - tech3 0.048 1.000 P value adjustment method: bonferroni

From the output we can see that the only p-value less than .05 is for the difference between technique and technique 3.

Thus, we would conclude that there is only a statistically significant difference in mean exam scores between students who used technique 1 and technique 3.

**The Holm Method**

The Holm method is also used when you have a set of planned pairwise comparisons you’d like to make beforehand and it tends to have even higher power than the Bonferroni method, so it’s often preferred.

We can use the following syntax in R to perform the Holm post-hoc method:

#perform the Holm post-hoc method pairwise.t.test(df$score, df$technique, p.adj='holm') Pairwise comparisons using t tests with pooled SD data: df$score and df$technique tech1 tech2 tech2 0.206 - tech3 0.048 0.384 P value adjustment method: holm

From the output we can see that the only p-value less than .05 is for the difference between technique and technique 3.

Thus, again we would conclude that there is only a statistically significant difference in mean exam scores between students who used technique 1 and technique 3.

**Additional Resources**

The following tutorials provide additional information about ANOVA’s and post-hoc tests:

How to Interpret the F-Value and P-Value in ANOVA

The Complete Guide: How to Report ANOVA Results

Tukey vs. Bonferroni vs. Scheffe: Which Test Should You Use?