Errors and Power in Hypothesis Testing | Statistics Tutorial #16 | MarinStatsLectures

[@marinstatlectures]

👋🏼 Hello Friends ! In this video we explore the concepts of Type I Errors (False Positive), Type II Errors (False Negative), and Power (Sensitivity) in hypothesis testing working through multiple examples and using a 2 by 2 table to understand these concepts better. If you like to support us you can Donate (bit.ly/2CWxnP2), Share our Videos, Leave us a Comment and Give us a Like 👍🏼 ! Either way We Thank You!

[@sayedbesmillahsultani227]

Are you a magician or something? I do not get bored even if I am to watch the whole playlist at once, yet I will understand the whole playlist. A galaxy of thanks to you for your well-designed lectures. By the way, I love your lectures and your cute kid.

[@roozbehqorbanian6029]

''hello guys" in PERSIAN!!!:)) thank you for the video

[@hannahz1514]

Thanks a lot!! Your video just save me!!!

[@unalumnomas2585]

Thank you so much! It's all really well explain. Life bless you :)

[@doio477]

Your videos are great! you're an excellent teacher

[@rodrigoserrano4766]

Thank you!

[@disciplinenepal5081]

good sir from nepal

[@yukijuliusng8551]

Left hander, writing backwards must be difficult while explaining about stats.

[@hamidkiangaikani]

they simply flip the video :|

[@felixh7699]

Shouldn't a type I error be a F.N. because your result is that H0 is negative when in fact it is positive (so you falsely reported negative) and vice versa?

[@marinstatlectures]

the terminology can be confusing at first. when "rejecting a null hypothesis", this is referred to as a "positive test result". "failing to reject the null" is a "negative test result" (much like disease testing, null is that you dont have disease, alternative is that you do have the disease, and testing positive means we reject the null and conclude that you have the disease, and vice versa). so a Type I error is when we reject the null when in reality it is true. when we reject the null this is a "positive test result"...and if in reality this is incorrect, it is a "false positive". hope that helps clarify it. i did find the terminology confusing at first. think of it this way...when getting tested for a disease, a "negative test result" is that you dont have the disease. while this isn't a negative thing to happen (it is good to not have the disease), the terminology is that you've tested negative for the disease (you dont have it). similarly, a "positive test result" means that we think you do have the disease. having the disease is not a positive thing to happen...but it means that you have tested positive for the disease. that is what caused men confusion for a bit when i first learned this stuff...

[@felixh7699]

@@marinstatlectures Thank you. It is confusing indeed but I think I got it now...

[@tedgq]

OJ trial was a type ll error

[@charanr5161]

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