In the field of statistics Occasionally we heard about categorical data, tests to validate the results, and different approaches to knowing about data. today we are here to learn about one of the statistical tests – the Chi-Square Test.
As data is growing day by day, everything – every decision, and behavior is validated with some machine learning algorithms, data science, NLP (Natural language processing ), and deep learning.
Chi-squared test, a statistical method, is used by machine learning engineers or statisticians to check the “goodness of fit test”. It is used to test if a sample of data came from a population with a specific distribution.
The chi-squared test determines the difference between observed and expected data. This test can also be used to determine whether it correlates to the categorical variables in our data. It helps to find out whether a difference between two categorical variables is due to chance or a relationship between them.
So the question is. – when we use the chi-square test
A chi-square test is a statistical test used to compare observed results with expected results. The purpose of this test is to determine if a difference between observed data and expected data is due to chance, or if it is due to a relationship between the variables you are studying.
Therefore, a chi-square test is an excellent choice to help us better understand and interpret the relationship between our two categorical variables.
Formula For Chi-Square Test
Where – x = Degrees of freedom
Uses of the Chi-Squared test
- The Chi-squared test can be used to see if your data follows a well-known theoretical probability distribution like the Normal or Poisson distribution.
- The Chi-squared test allows you to assess your trained regression model’s goodness of fit on the training, validation, and test data sets.
Types of Chi-square test:
- Goodness of fit test – Used to determine whether or not a categorical variable follows a hypothesized distribution.
- Test of independence – Used to determine whether or not there is a significant association between two categorical variables.
How to perform the chi-square test
- Define your null and alternative hypotheses before collecting your data.
- Decide on the alpha value. This involves deciding the risk you are willing to take of drawing the wrong conclusion. For example, suppose you set α=0.05 when testing for independence. Here, you have decided on a 5% risk of concluding the two variables are independent when in reality they are not.
- Check the data for errors.
- Check the assumptions for the test. (Visit the pages for each test type for more detail on assumptions.)
- Perform the test and draw your conclusion.
In the next article, we will observe the practical for the chi-square test.