Statistics : T-test & Chi-Square test


In statistics we have various types of test to validate the a single group or multiple groups.Here we can say a group as a feature in the data set.These features can be either categorical or numerical features.

With the help of T-test and Chi-square test we can conclude that if the given group/feature’s are independent or dependent with each other.

T-test :

When we want to compare one or two numerical variable for independence we use T-test.Git Link

It is of three types.

1-Sample T-test
2-Sample T-Test or Independent T-test
3-Paired T-test

1-Sample T-test

To compare population mean and sample means if they are same or different we use 1-Sample T-test.

Below code to test in Python.

2-Sample T-Test or Independent T-test

Sample T-test will compare two independent means of two features to validate if both are same or different

3-Paired T-test

We go for paired t-test when we want to compare different means of same group taken at different time interval.

Chi-Square test:

Chi-Square test is used to compare two categorical variables to see if they are same or different .Git Link

Conclusion: These test are very important in data Preprocessing and feature engineering steps where we drop the columns which depict same information to avoid multicollinearity issue.

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Anjani Kumar

Anjani Kumar

Data Science ,ML & NLP Enthusiastic

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