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

Courses on Khan Academy are always 100% free. Start practicing-and saving your progress-now: www.khanacadem...
Introduction to Type I and Type II errors in significance testing. Significance levels as the probability of making a Type I error.
View more lessons or practice this subject at www.khanacademy...
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!
Donate here: www.khanacadem...
Volunteer here: www.khanacadem...

[@greensky01]

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)

[@prinkasays]

Best way to remember this

[@sebster95]

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)

[@Mkhl4Sure]

I love this explanation

[@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 :')

[@rajvaswani7878]

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.

[@muyin]

you call that easy??

[@davidsweeney111]

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

[@kidhuman1]

2022 using this for poli sci stats thank you my man

[@sapnaratheesh2813]

Pl keep it precise and simple

[@teacher.prashantdubeyforma5142]

clear presentation

[@Posby95]

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

[@jonathansiegel2818]

The reason for this is that we NEVER know whether the null hypothesis is or is not true. This is because the null hypothesis is stating what the population parameter is (for example, the average score for all individuals between age 18 and 24 in the entire world. We don't know the answer to that. We assert a population value.. and then take a sample and compare the scores of that sample to our estimated population mean). If the sample mean is far off into the tails of our standardized normal distribution, we reject the null hypothesis (the statement about our population parameter).

[@kwicke5636]

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.

[@goryluv]

Looks like a confusion matrix

[@angelayang5183]

thank you!

[@medicalexercisephysiologys2818]

Good

[@Carmadon100]

better than my professor

[@aayangoverski3437]

1:44

[@nirmanonline]

Superb.. Easy to understand. Thanks

[@jagadeeshmandala4097]

Playlist link

[@MrWinter2]

Thank you for posting! :)

[@goddaniel9478]

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

[@dankormick7476]

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

[@lxvedrops]

Whats NH

[@rileyregan2900]

This video could have just started at 3:00 lol

[@ishikahalder799]

🔥🔥🔥🔥🔥

[@lucyhudson3921]

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?

[@amineamine3374]

C,est quoi ça

[@siegfriedcrypto]

Amine Amine Les types d'erreur des probabilités..

[@zachaiedmonds6773]

first

[@SuperYtc1]

First