5 Best Strategies to Work on Analysis of Variance

Analysis of variance, or ANOVA, is a statistical method used to compare the means of different samples collected from a population. This method allows the researcher to determine if the sample means are equal or not. Most of the time, an ANOVA test is performed to approve or disapprove of a hypothesis formulated at the start of the research study. Despite being a very important test in testing hypotheses, many students do not have the idea of performing this important statistical test. They just do not know the best strategies to work on the analysis of variance. Keeping this in mind, in today’s post, we will unpack the 5 best strategies or steps to perform the ANOVA test. Before that, let’s start with its definition. 

What is an analysis of variance (ANOVA) test? 

ANOVA is nothing but a statistical test used to determine if there is a statistically significant difference between the means of three or more categorical groups. This test is mostly used to have an idea of the influence that independent variables have on the dependent variables in a regression study. It basically splits the independent variable into two groups. One of those groups influences the dependent variable, and the other is used as a control group and does not have any impact on the dependent variable. Normally, there are two types of ANOVA tests used to determine the differences between means. A brief description of each is as follows: 

One-way ANOVA test 

One-way ANOVA or single-factor analysis of variance test is the first type of ANOVA test. As the name suggests, this test is used when there is only one independent variable with two or more levels of the dependent variable. For example, the dependent variable may be in what month of the year there are more roses in the garden. Here, the levels are twelve because there are twelve months in a year. 

Two-way or factorial ANOVA test 

Factorial ANOVA is the second type of analysis of variance test. In this test, there are two or more categorical independent variables and a continuous dependent variable. The two independent variables divide the cases into levels or groups, and the researcher studies the impact of independent variables. It is important to note that each sample in the two groups is unique and has no crossover with any other sample. 

5 best strategies to work on the analysis of variance 

After reading the information above, you have got a full idea of the ANOVA test and its types. However, to perform this test, you must also know its steps or strategies. Hence, a brief description of the 5 best strategies, in this case, is as follows: 

  • Calculate all the means 

Before you run the analysis, it is important that you calculate all the means of the groups involved in the test. It is the first strategy to carry out a result-oriented analysis of variance test. After calculating the individual means, do not forget to calculate the overall mean of the whole data combined too. 

  • Set up the null, alternate hypothesis and alpha 

The next strategy is to set up the null and alternate hypotheses of your ANOVA test. The null hypothesis states that there are no significant statistical differences between the means, and all means of the groups are the same. The alternate hypothesis is the opposite of this and states that the means of the groups are different. 

  • Calculate the sum of squares 

Once the hypotheses are set up, the next strategy is to calculate the sum of squares. You need to calculate two kinds of the sum of squares. One is SST, and the other is SSW. SST is the sum of squares of the total, which is calculated by squaring the whole dataset. On the other hand, SWT is calculated by squaring each group involved in the analysis of variance test. If you are not sure how to calculate these sums, go and get help from assignment writing services

  • Calculate the degree of freedom and mean squares 

The next step is about calculating the degree of freedom (df) and mean squares. As an analyst, you must calculate the degree of freedom of the whole dataset and individual groups. The same also goes for the mean squares. 

  • Calculate F value and compare it with tabulated F 

The last strategy to work on the analysis of variance test is to calculate the F value and compare that value with tabulated F value. If your calculated value is greater than the critical or tabulated value, the null hypothesis is rejected. As an analyst, you conclude that there is a significant difference between the means of groups. 


Conclusively, the analysis of variance or ANOVA test is an important test to do to determine the differences in the means of three or more groups. The top 5 strategies mentioned above explain the whole ANOVA test procedure and ways to do it. So, follow them closely to perform a good ANOVA test.

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