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Statistical Tests for Data Scientists: A Practical Guide with Code Examples
Statistics are an integral part of data analysis across various industries. In data science, you’ll use statistical tests to learn about your data, test your hypothesis or pick the best features for your model. Statistical tests allow data scientists to determine whether observed differences or relationships in data are likely due to chance or if they are statistically significant. However, understanding and applying statistical tests can be a daunting task, especially for beginners!
In this article, we will explore five commonly used statistical tests and provide a practical guide on how to perform them using Python and popular statistical libraries such as scipy and statsmodels. I will not go into too much detail on the test’s formulas and how to code the statistical tests from scratch. Instead, I will walk you through the process of conducting these tests and interpreting their results. I will cover the following tests: T-test, ANOVA, Chi-Square, Simple Linear Regression and Pearson’s correlation. This guide is intended for anyone who wants to develop their skills in data analysis and apply statistical tests to their datasets!
1. T-test
A t-test is a statistical hypothesis test used to determine whether there is a significant difference between the means of…
