Hypothesis testing is often used to facilitate statistical decisions using experimental datasets. The testing is used to validate assumptions about a population parameter. For example, consider the following statements:

• The average score of students taking the Machine Learning course at the University of Nepal is 78.
• The average height of boys is higher than that of girls among the students taking the Machine Learning course.

In all these examples, we assume some statistical facts to prove those statements. A situation like this is where hypothesis testing helps. A hypothesis test assesses two mutually exclusive statements about any particular population and determines which statement is best established by the sample data. Here, we used two essential keywords: population and sample. A population includes all the elements from a set of data, whereas a sample consists of one or more observations taken from any particular population. 

In the next section, we are going to discuss hypothesis testing and discuss how we can use Python libraries to perform hypothesis testing.