In this lesson, we'll learn the proper framework to create hypotheses and test them. Unsurprisingly, this framework is called hypothesis testing. This framework is based on a rigorous mathematical foundation and has served as the guiding force for researchers and data scientists alike on their experiments. Developing hypotheses and learning how to test them are critical skills for data scientists. Data scientists leverage data to guide company strategy and make improvements to their products. Once mastered, data-driven decisions can start to be properly made.
Some examples of hypothesis problems that data scientists face include:
- If a new ad on a website caused a meaningful increase in user engagement.
- If raising the price of a product caused a meaningful drop in sales.
- If a new weight loss pill helped people lose more weight.
As you go through this lesson, you will learn concepts such as the null hypothesis, alternative hypothesis, test statistics and more. You will also learn how to conduct a two sample independent t-test. This lesson will also touch on the concept of p-values and why they're a critical concept in hypothesis testing. By the end of this lesson, you will feel extremely confident in your hypothesis testing abilities and you will be able to help make data-driven decisions in any data analyst or data scientist position.
As you learn about hypothesis testing, you’ll get to apply what you’ve learned from within your browser so that there's no need to use your own machine to do the exercises. The Python environment inside of this course includes answer checking so you can ensure that you've fully mastered each concept before learning the next concept.
1. Hypothesis and Hypothesis Testing
2. Null & Alternative Hypothesis
3. Walking Through An Experiment
4. Comparing Groups Visually
5. Restating Our Hypotheses
6. Looking at Differences
7. The Two Sample Independent t-test
9. The Process of Hypothesis Testing
10. Next steps