Type 1 and Type 2 errors by Daniel Lakens

In a Neyman-Pearson approach to statistical inferences the goal is to make decisions while controlling errors in the long run. In this video I explain what Type 1 and Type 2 errors are. We will discuss which outcome (a true postive, true negative, false positive, or false negative) is the most likely outcome in your next study, and why you should justify your error rates. This video is part of my free online course on statistics: https://www.coursera.org/learn/statis... Can't get enough? Some suggestions for additional reading: An accessible introduction to hypothesis testing, Type 1 and Type 2 errors, power, and p-values can be found in most textbooks, but a clear open access summary is available in: Banerjee, A., Chitnis, U. B., Jadhav, S. L., Bhawalkar, J. S., & Chaudhury, S. (2009). Hypothesis testing, type I and type II errors. Industrial Psychiatry Journal, 18(2), 127.