Introduction to power in significance tests | AP Statistics | Khan Academy
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- เผยแพร่เมื่อ 12 ก.ย. 2024
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Introduction to power in significance tests.
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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.
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God it’s so clear I’m in tears
战忽局的同志们集合了
@@WA-hq6ls wot
lmao
😂😂😂😂
@@WA-hq6lsthe google translation says: "Comrades from the War Ignorance Bureau have gathered."
Finally, as a beginner, I can understand why the significance threshold increases, would cause the power increase. Thank you for sharing this super clear explanation!
I have seen so many videos on this in german and now decided to watch an english one and this one just taught me everything I needed to know in less than 10 minutes
yeah germans are too german
Sal literally knows everything
I think the vocabulary gets in the way of explaining power. In general what is missing is an intuitive explanation preceding all the Type I/II error, effect size, alpha, etc. Like, "power is how we quantify the likelihood that your sample has all the properties of the population", with some illustrations.
Perfectly taught. Thank you so much.
It's exactly what I need
Very clear explanation and too the point. Thank you
7:46 by increasing a, the type 2 error increases not the type 1 error.
Perhaps this is pedantic, but 1 - P(type II error) isn't exactly the same thing as P(not making a type II error) -- it's more like P(not making a type II error | H0 false). For example, if H0 is true, then we _can't_ make a type II error, but it shouldn't be included in this probability.
he always delivers
I am doing summer homework rn and this is so helpful.
Thank you so so much, I finally understood what this is
You try to give the video more brightness it will be great if you do
8:00 increase sample size will not change the distribution curve, the description is so misleading.
Bless you Mr. Khan!
Life saver
Thank you much! very good explanation
this is called "Power" of Khan academy
First love your channel
really good ! So clear!
insanely great stuff.
no one could have explained this any better.
thank you so much
Doesn't the probability of not making a Type II error (1:50) consist of all 3 other possible cases - i.e. also the probability of making a Type I error and correctly failing to reject the null hypothesis? Shouldn't it be P(not making Type II error | H0 is false)?
You are amazing. Thank you
We wish to test H₁ = 100 against H₁100 using a sample of size n = 16 from a n (u; ²) distribution. Suppose that we know that the true mean is μ = 100 + 20. (i) What will the power of the test be if we test at the 5% level of significance?
8:15
Explained really excellent, but the sketch is not clear
Thank u
May be because I am watching the video on a relatively smaller screen (iPhone Pro max 15) instead of larger screen.
The Alpha goes up power goes on the sketch is not clear.
thank you so so so much for this video !
u saved my life
thanks a lot for the video!
Thanks great video power concept.
Couldn’t u also explain p value concept, With height & heads & tails examples
Why p value is preferred over critical value & z or t statistic in hypothesis testing? or why use p value?
Does it say or determine the actual probability is of lesser significance to reject the belief about the population parameter or mean? Is this the reason p value method is valuable & more reliable?
Thanks 😊
😊
Hey, did you find the answer to why p value is preferred over critical value ?
Excellent and clear explanation
amazin
yur
#unexpectedMewTwo
Its just random since its a example of when mew not equal to mew one
great video! would it be possible to add it to the AP Stats portion of Khan Academy? Thanks.
It's in there now.
Here on the morning of ap test
Make a video about Componendo and Dividendo
give me the formula
This has to be one of the most confusing things ever
So, I guess I am the only one who still doesn't understand what the power is after watching this video... Gosh why am I even studying stats for, I am just not built for it!
Would have liked him to actually calculate power instead of saying its complicated
mew 2
I thought I was dead....
This still makes no sense. I don't get it
Try to understand what a Type 1 & Type 2 error is first. They are super key for this topic.
Once you understand the errors then think about the term 'assumption'. If H0 (null hypothesis) is false then there exists an alternative hypothesis H1 that is true. H1 will have an entirely different distribution and mean (Mu). Therefore wherever the null hypothesis distribution doesn't intersect with the alternative hypothesis distribution is considered to be the statistical power.
Typically the statistical power's p-value should be 80%.
mew two
bad explanation. don't get anything
It's actually a pretty good explanation but if you don't understand some of the fundamentals I can understand it'd be confusing. I'd go back earlier in the AP stats course in Khan academy so you can conceptualise standard deviation, effect size, type 1 and type 2 errors more intuitively....all 4 of these variables are involved in a power calculation since the sample size is related to the standard deviation
Michael Matula hehe thank God I don’t need this info anymore. Lol
@@DarkMelodies-hv9wf why is that?
Michael Matula it was for a course I had in my 1st year or sth