Hypothesis testing is a very crucial part of Data science. The main aim of this study is to predict the behavior of the population data. Many a times population data is not readily available. In that case instead of population data we are left with sample data. Now in order to generalize the sample data to population data we usually we try to model the data using some statistical models.

Now given a sample data we have our assumptions. Hypothesis testing is a systematic statistical method which helps to reject or accept our assumptions. Our basic assumptions are known as 'Null Hypothesis' and the alternate assumptions are known as 'Alternative Hypothesis'.

Now, depending on the sample size, availability of the statistical parameters (e.g. mean, standard deviation) of population or sample data, there are many statistical tests are available, which helps us to get a firm conclusion.

In this repository, the noebook contains, several worked out examples of Hypothesis testing.

Hope this helps.

This repository contains different statistical methods for Hypothesis testing.