Why is the SE calculated from 1 sample a good approximation for the spread of many means of hypothetical samples (samples that we don't take in reality?)
The confidence interval is double-sided, meaning there is 50% on each side of the mean, which adds up to 100% confidence interval in total. So if it's 95%, there is 47.5% on either side of the mean. That means 2.5% is left out of the confidence interval on either side of the mean. So you use 1.96 instead of 1.645. A value of 1.645 is used when 5% is left out on either side. I know it's a bit too late for a reply, but hope this helps others as well.
This really helped! I like how you repeated what exactly the curve meant.
Thank you so much dude I spent so much time trying to figure this out and this is like the most clear explanation for it
Extremely well structured and cogent explanation
THANK YOU!
thank you very much for nice "lecture"
Great videos!!
Very nicely explained
this was the one.. this was the one where it all clicked
My thoughts exactly!!
bestt
Why is the SE calculated from 1 sample a good approximation for the spread of many means of hypothetical samples (samples that we don't take in reality?)
Thanks for the explanation.
Can the Prof.Smith or any other stat expert here please explain why he took 1.96 and not 1.645 against the 95% CI?
The confidence interval is double-sided, meaning there is 50% on each side of the mean, which adds up to 100% confidence interval in total. So if it's 95%, there is 47.5% on either side of the mean. That means 2.5% is left out of the confidence interval on either side of the mean. So you use 1.96 instead of 1.645. A value of 1.645 is used when 5% is left out on either side.
I know it's a bit too late for a reply, but hope this helps others as well.