Type I error vs Type II error

👉🏻 Download Our Free Data Science Career Guide: https://bit.ly/33OZz4n
👉🏻 Sign up for Our Complete Data Science Training with 57% OFF: https://bit.ly/33fqBkd

In this lesson, we will learn about the errors that can be made in hypothesis testing. Type I error is when you reject a true null hypothesis and is the more serious error. It is also called ‘a false positive’. The probability of making this error is alpha – the level of significance. Since you, the researcher, choose the alpha, the responsibility for making this error lies solely on you.

Type II error is when you accept a false null hypothesis. The probability of making this error is denoted by beta. Beta depends mainly on sample size and population variance. So, if your topic is difficult to test due to hard sampling or has high variability, it is more likely to make this type of error. As you can imagine, if the data set is hard to test, it is not your fault, so Type II error is considered a smaller problem.

► Consider hitting the SUBSCRIBE button if you LIKE the content: https://www.youtube.com/c/365DataScience?sub_confirmation=1

► VISIT our website: https://bit.ly/365ds

🤝 Connect with us LinkedIn: https://www.linkedin.com/company/365datascience/

365 Data Science is an online educational career website that offers the incredible opportunity to find your way into the data science world no matter your previous knowledge and experience. We have prepared numerous courses that suit the needs of aspiring BI analysts, Data analysts and Data scientists.

We at 365 Data Science are committed educators who believe that curiosity should not be hindered by inability to access good learning resources. This is why we focus all our efforts on creating high-quality educational content which anyone can access online.

Check out our Data Science Career guides: https://www.youtube.com/playlist?list=PLaFfQroTgZnyQFq4nUfb-w2vEopN3ULMb

#NullHypothesis #DataScience #Statistics

Видео Type I error vs Type II error канала 365 Data Science