In the field of Data Science, Hypothesis Testing is an essential tool of statistical inference that plays a vital role in
making an informed conclusion about the population using the sample data. It helps in making a decision as to which sample
data best support mutually exclusive statement about the population. It is one of the essential concepts in statistics that form the
foundation of Machine Learning because it is how you make decision if something happened, or if specific treatments have any
side effects, or if groups differ from each other or if one variable forecast another and conclude about the characteristics of what
you're comparing. However, while dealing with the Hypothesis Testing problems, the chances and probabilities for errors are
uttermost. Mistakes created within the statement of the null and alternative hypothesis will have a severe impact within the
interpretation of the result, which may lead to false or inaccurate decisions.
