Search⌘ K
AI Features
Home
Courses
An Introductory Guide to Data Science and Machine Learning

Statistical Hypothesis Testing

Explore the core principles of statistical hypothesis testing, including formulating null and alternative hypotheses, understanding Type 1 and Type 2 errors, and applying p-value and critical-value approaches. Gain practical insights on one-tailed and two-tailed tests and how to use t-tests for comparing data samples. This lesson builds your foundation for making informed decisions based on statistical evidence in data science.

We'll cover the following...

Statistical hypothesis testing

What is a hypothesis?

A hypothesis is a testable statement, which is tested using some investigation or experimentation. We make specific assumptions about the dataset, which is called the hypothesis, and they are further accepted or rejected using some methods.

Hypothesis testing

Hypothesis testing is a statistical analysis that uses sample data to assess two mutually exclusive theories about the properties of a population. We make a hypothesis about the dataset at hand and ask different questions. Hypothesis testing helps us to find the likelihood of answers to those questions that we ask.

These methods produce output that helps us to further accept or reject the assumption made about the dataset.

Null hypothesis (H0)

The assumption we make initially about the dataset and the statistical test is called the null hypothesis.

Alternative hypothesis (H1)

The alternative assumption holds the assumption we make after the experimentation fails.

We will show you an example to further clarify the definitions of the above terms.

Type 1 error

This error happens when we fail to accept the null hypothesis, meaning we reject the null hypothesis when it should be accepted. It is also called a false positive. The probability of committing a Type 1 error is called the significance level, and it is denoted by α\alpha.

Type 2 error

This error happens when we fail to reject the null hypothesis, meaning we accept the null hypothesis when it should be rejected. It is also called a false negative. The probability of committing a Type 2 error is denoted by β\beta ...