Introduction to Type I and Type II errors | AP Statistics | Khan Academy

Introduction to Type I and Type II errors in significance testing. Significance levels as the probability of making a Type I error.

AP Statistics on Khan Academy: Meet one of our writers for AP¨_ Statistics, Jeff. A former high school teacher for 10 years in Kalamazoo, Michigan, Jeff taught Algebra 1, Geometry, Algebra 2, Introductory Statistics, and AP¨_ Statistics. Today he's hard at work creating new exercises and articles for AP¨_ Statistics.

Khan Academy is a nonprofit organization with the mission of providing a free, world-class education for anyone, anywhere. We offer quizzes, questions, instructional videos, and articles on a range of academic subjects, including math, biology, chemistry, physics, history, economics, finance, grammar, preschool learning, and more. We provide teachers with tools and data so they can help their students develop the skills, habits, and mindsets for success in school and beyond. Khan Academy has been translated into dozens of languages, and 15 million people around the globe learn on Khan Academy every month. As a 501(c)(3) nonprofit organization, we would love your help! Donate or volunteer today!

[greensky]

Something to help remember this:
Benjamin Franklin stated it as: "it is better 100 guilty Persons should escape than that one innocent Person should suffer".
Where:
1) One innocent person suffer = type 1 error (alpha error) = more severe [false positive]
2) 100 guilty persons escape = type 2 error (beta error) [false negative]
3) You have more authority (increase Power) in a prison when you reduce type 2 error
a) by having a bigger prison (larger sample size)
and
b)counting prisoners (increase precision of measurement)

[sebster]

Type I error = Illusion (you are seeing an effect when there is not one)
Type 2 error = 2 blind 2 see (you are failing to see an effect when there is one)

[AlphaFoxDelta]

We never try to prove that something is false, that isn't and shouldn't be your goal. If this weren't the case, you would spend eternity proving every possible case wrong. We simply collect evidence and use reason to prove that something may be true with a certain level of confidence, a level which is rarely 100%.

[ChristianneAngelica]

I just watched the entire stats series... thank you sir for making me pass this class :')

[rajvaswani]

Easy way to remember type 1 and 2 errors:
Type 1 includes explicit costs and is an error of commision (committing the wrongful)
Type 2 includes implicit (opportunity) costs and is an error of ommision (not doing what was right)
Type 1 errors are often more serious as they're explicit and thus more transparent. Often avoiding one error comes at the cost of committing another, weigh in which error matter the most in your scenario.

[davidsweeney]

superb informative, exception clear presentation of fascinating concepts, appreciate it buddy!

[kidhuman]

2022 using this for poli sci stats thank you my man

[kwicke]

superb explanation and visualisation! Thank you.

[simonreichmd]

I believe p value is not equal to the probability that null hypothesis is true. To my knowledge it is a probability that we would observe to the same or more extreme results despite null hypothesis being true.

[Posby]

Why not just say «accept Ho» instead of «fail to reject Ho»? Seems like a confusing double negative for no reason.

[goddaniel]

I didn't know VLAD TV made Khan Academy videos....

[rileyregan]

This video could have just started at 3:00 lol

[dankormick]

If Ho is low NH must go, If Ho is high NH is your guy

[lucyhudson]

why is he talking about P values if there is an alternative hypothesis? This is a mix up of Fishers Significance testing and Neyman Pearsons Hypothesis testing which aren't compatible?