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What Factors Affect the Power of a Z Test?
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- Опубликовано: 18 фев 2013
- A look at what factors influence the power of a hypothesis test. This discussion is in the setting of a one-sample Z test on the population mean, but the concepts hold for many other types of test as well. I discuss what factors affect power, and illustrate the concepts visually using various plots. There are no power calculations carried out in this video; I have another video on calculating power: • Calculating Power and ...
This is the best set of videos on any subject on the internet, I do not say that lightly and am forever indebted to your hardwork, thank you for taking your time to make these videos
You are welcome again! I try to be concise, while staying true to statistics and getting the important information across. I'm glad you find my videos useful. Cheers.
You are very welcome Jeffrey! I very much appreciate the compliment!
Thank you my friend. You are an excellent instructor. I have never seen someone like you who takes you to the point of any discussion. You are a top noch.
Thank you very much for your wonderful compliment! I am very glad to be of help.
Amazing visualization as always. Thank you!
You're welcome! I'm glad you found it helpful.
This is so great! Thank you so much for posting this. It's been tremendously insightful for me.
Hi z4er0s. All of the plots in all of my videos were created in R. Cheers.
Great visualization! Thank you!
Sir...Respect!!!, your videos are on point...thank you very much
Excellent class!!!
thanks your videos are awesome! :D
great video！！
My life can’t without you
beautiful baritone singing voice, my friend.
no words sir ji
Can anyone explain the intuition behind how change in standard deviation affects the power of the test? I understand that it compresses the distribution and "squishes it inward", which allows for more area under the curve, but I'm not sure how to phrase that in plain English.
Wait I think I got it. A smaller sample standard deviation decreases the range of the confidence interval. Therefore, there's a greater chance of seeing an extreme value, which means rejecting the null hypothesis.
@@mantistoboggan537 With a smaller sigma, the sampling distribution of the statistic becomes less dispersed (you're right - it gets squished inward), and thus we have fewer possible values (that the statistic can take on) in the acceptance region. This makes it easier to reject the null hypothesis, thereby increasing the power of the test.
A warm cup of coffee and some good-ass statistics... doesn't get any better than this!
I don't often hear that, but I'm glad to hear it!
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It does look a little minty and refreshing.