Do you remember the scientific process as you learned it in high school? It all starts with a problem for which you hypothesize an outcome. Next, you test your hypothesis, and experimentation either proves, disproves, supports, or opposes your hypothesis.
As you can imagine, this process is used extensively in data science. In this article, we’ll discuss ANOVA and what it has to do with hypotheses and testing. Let’s get scientific!
An ANOVA is a statistical model.
If you’re not a data scientist or statistician, you’re probably wondering, “What is an Anova?” ANOVA is more than another cool acronym—it’s a statistical method for finding the variance of group means for two or more groups. In other words, it tests for standard deviation in different test subjects.
Let’s say we’re testing the average temperature of each month over the course of a year to determine the hottest month of the year. We could use ANOVA to find the hottest and coldest months and determine by how much they’re warmer and colder than other months. Analysis of variance would test the difference in the mean or average temperatures of each month and the answer to that query would be the F statistic.
ANOVA tests the impact of an independent variable on a dependent variable.
There are plenty of uses for the ANOVA methodology. One of the most common uses of ANOVA is finding the impact of an independent variable on a dependent variable. This is also known as the interaction effect.
Finding out the effects of an independent variable on a dependent variable is one of the ways they test the efficacy of drugs in the pharmaceutical industry. As you know, there’s the control group and the placebo group. The independent variables in these tests are the actual drug the pharmacists are testing and the placebo.
Using the ANOVA test, they’re able to determine the effects of the drug on the control group in comparison to the effects of the placebo on the other group. ANOVA will give them a single numeric value for the variance of the drug, and the pharmaceutical company can move on from there to the next phase of testing.
There are different types of ANOVA tests.
Different situations call for different testing models. That’s why there are several types of ANOVA tests for different situations and needs. There are tests for programming machine learning algorithms, using different data models, and more.
The one-way ANOVA test is ideal for when you only have a single factor and different levels of that same factor. For instance, if you’re testing how much your plants grow during different months, the different months are the factor, and each month is a level. Factorial ANOVA tests are ideal for when you have multiple factors, such as placebo and actual drug for drug testing or fertilizer and no fertilizer for testing a fertilizer. Factorial ANOVA tests factors in all the possible independent elements that could sway the dependent variable.
The ANOVA test is one of the most popular and reliable testing models there is. It’s a statistical method for testing the variance between means of different groups. If there’s a significant difference, it could be the beginning of something great. It could mean new treatments for COVID-19, cancer, or even depression. Indeed, a significant level of variance could be the start of something great.
You’ll run plenty of ANOVA tests if you go into data science, especially machine learning. Data scientists use ANOVA tests for training algorithms and testing the efficacy of data models. So, learning how to use ANOVA to determine variance and statistical significance could help you immensely in your career.