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Analysis of Variance Testing (ANOVA) is a way to test different means and their procedures against each other. Due to its design lending, itself to being used in multiple groups it is a popular method for testing. ANOVA tests both the null and alternative hypothesis in one test, just like a T-test (meant for only two means).

What Does a T-test do?

Since ANOVA testing is a larger scale T-test it is important to grasp what they do. A T-test simply put is a statistical analysis of two sample groups means. It looks at the two populations and determines if they are very different from each other. T-tests only work with small groups as well. Due to these constraints, it sometimes takes multiple T-tests in larger groups. While a T-test provides useful data that can help prove hypothesis it comes at a risk. Increasing the number of tests, increases calculations and time it takes to test a hypothesis. It also leaves room for more error. This risk leads way to larger format T-test in the ANOVA test.

ANOVA: The Basics

ANOVA’s statistical model compares the means of more than two groups of data. Just like a T-test it analyzes the group’s procedures against each other. These procedures or variables are analyzed to gauge if they are significantly different. Due to it being designed for multiple groups it is preferred over conducting multiple 2 group T-tests. ANOVA tests produce less errors in these larger groups and usually takes a shorter amount of time to test. They are general tests though. ANOVA tests are meant to test general hypothesis, they do not work for a hypothesis with a narrow scope.

Interpreting ANOVA Statistics

When an ANOVA test is conducted, there are values that can interpret whether a null hypothesis can be proven. The null hypothesis that is being tested is to prove whether or not the populations are equal. The populations are given different variables to see if they are equal. A significance level of 5% or lower shows a difference exists between the populations.

When you take the probability of a result from a p-test you get the p-value. If that value is less than the significance level, such as 0.05 for a 95% confidence, the value of the hypothesis can be proven true. That means there is a difference in populations based on variables. If the p-value is greater than the significance level the null hypothesis cannot be proven. This means that there is not enough evidence to suggest any difference.

In larger group sets it is important to group data to determine its significance in rejecting a null hypothesis. Confidence intervals can be used to determine the difference between groups. A confidence interval is simply but the level of uncertainty with a population.


Since ANOVA tests can interpret a population’s means based on variances, it has many practical uses in a laboratory setting. There are many standards required for a laboratory to be verified to be in working order. An ANOVA test can help predict any issues that might arise from implementing certain procedures. It can make scientifically appropriate predications based on variables given. An ANOVA test can detect any deficiencies in a practice or procedure. Eliminating those practices or changing how they are implemented can contribute to the overall professionalism of the lab. From the changes made from an ANOVA test a lab can be more productive and efficient.

Example of Applying an ANOVA Test in Lab Setting

ANOVA is designed to compare multiple populations making it much more user friendly that using a Ttest in this setting. For instance, an analysis of a analyte that has been done for quite some time now has 4 different test methods. For a lab to determine which works best and will produce most acceptable results, multiple T-tests will have to be done to determine what the outcome would be.

Let us say a lab is asked to preform that analysis while they are trying to figure out the best way to do it. They will have to use the method they have been using whether or not it is the most efficient way to do it. If it is less efficient it might take too much time could turn to loss of business. Using the wrong procedure could also produce a faulty outcome.

With an ANOVA test, only one test is needed for all of the ways a test method can be done. Having to do only one test eliminates all the time wasted on multiple T-tests. This produces the best solution for which procedure to use. This will eliminate unnecessary procedures and lets labs use the most efficient and current method possible.




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