Key Points

• Dunnet’s 1-way ANOVA is a test for comparing multiple groups.
• It is similar to a basic ANOVA but compares more than 2 groups.
• Dunnett’s 1-way ANOVA is similar to Tukey’s test or Fisher’s 1-way ANOVA.

When testing if the means of greater than 2 groups are statistically different you could use a 1-way ANOVA. But, if you have multiple treatment groups, then you can use Dunnett’s 1-way ANOVA. 

ANOVA stands for Analysis of Variance. ANOVA is a statistical method used to compare the means of two or more groups of data. It is based on the comparison of the variability between the different groups to the variability within each group. The method aims to determine if the observed differences between the groups are due to chance or some systematic effect. 

ANOVA tests the null hypothesis (Ho) that there is no significant difference between the means of the groups against the alternative hypothesis (Ha) that at least one group has a different mean than the others.

The test calculates an F-statistic, which is the ratio of the between-group variation to the within-group variation. If the calculated F-statistic exceeds a critical value, the null hypothesis is rejected, and you can conclude that at least one group’s mean is significantly different from the others.

What Is Dunnet’s 1-way ANOVA

Dunnett’s 1-way ANOVA is a statistical method used to compare the means of multiple data groups to a control group while controlling for the overall type I error rate. It is a variation of the one-way ANOVA test. ANOVA is used to compare the means of more than two groups. Fisher’s one-way ANOVA and Tukey’s one-way ANOVA are similar tests.

In Dunnett’s test, the control group serves as a reference or comparison group against which the other groups are compared. The test assumes that the observations in each group are independent and identically distributed and that the variances of the groups are equal.

The test calculates a test statistic that measures the difference between each group’s mean and the control group’s mean. It then compares it to a critical value based on the number of groups and the chosen significance level.

If the test statistic exceeds the critical value, the null hypothesis (that the means of all groups are equal) is rejected. You can conclude that at least one group’s mean is statistically different from the control group’s mean.

Why It Matters

Any statistical method fails to paint a complete picture. As such, you’ll have to rely on more than one tool to make the most of your data points. Further, the various ANOVA tests we’ve covered in the past have specific use cases. When you’ve got more than two sets of data that you’re directly comparing, this is where Dunnett’s 1-way ANOVA shines.

By using Dunnett’s you’re readily equipping yourself with the means of analyzing multiple data sets, as we’ll see in the industry example below.

An Industry Example of Dunnett’s 1-way ANOVA

Suppose a pharmaceutical company is testing the effectiveness of a new drug for lowering blood pressure. They recruit 4 groups of patients: a control group receiving a placebo. Three treatment groups received different doses of the new drug.

The company wants to determine if any of the treatment groups have a significant effect on blood pressure compared to the control group. They measure the blood pressure of 10 patients in each group and obtain the following data:

• Control group: 130, 140, 135, 145, 140, 135, 130, 138, 142, 140
• First treatment group: 125, 130, 128, 132, 135, 130, 135, 132, 138, 133
• Second treatment group: 118, 122, 125, 128, 130, 126, 132, 129, 133, 127
• Third treatment group: 115, 118, 120, 122, 125, 128, 123, 127, 129, 126

To analyze these data using Dunnett’s test, you start by calculating the overall mean and variance of the groups. Next, you calculate the test statistic for each treatment group by comparing its mean to the control group’s mean. Finally, you compare each test statistic to the critical value for Dunnett’s test, which depends on the chosen significance level (e.g., 0.05) and the number of treatment groups (e.g., 3). For a significance level of 0.05 and 3 treatment groups, the critical value is 2.92.

When all your calculations are done, you will conclude that treatment groups 2 and 3 significantly lower blood pressure compared to the control group. You cannot conclude that there is an important difference between treatment groups 2 and 3. You can note that they are significantly different from the control group.

Other Useful Tools and Concepts

If you’re new to the world of statistical analysis, then there is plenty to learn. Understanding the F test is essential. This test allows you to check for variances against multiple sample groups with ease. As such, our guide on the matter is required reading.

While we’ve touched on ANOVA to some extent, it hasn’t been exhaustive. However, we have a simple article that covers the ins and outs of ANOVA and how to perform one of these tests on your own. If you’ve been struggling with the concept, you might as well learn the right way of doing it.