R Programming for Statistics and Data Science - Hypothesis Testing

The video tutorial covers the topic of hypothesis testing, explaining the six steps in data-driven decision making. It introduces the concept of a hypothesis, differentiating between alternative and null hypotheses, and provides examples to illustrate these concepts. The process of hypothesis testing is detailed, including the importance of the null hypothesis and the inability to prove the alternative hypothesis. The tutorial also explains the ingredients of hypothesis testing, such as the rejection region and significance level, and discusses the differences between two-tailed and one-tailed tests.

Explain the concept of the rejection region in hypothesis testing.

What is the significance level (alpha) in hypothesis testing?

What are the differences between one-tailed and two-tailed tests?