"The bigger the sampling error, the bigger the confidence interval."

#1
This statement was highlighted as true in an exam practice paper.

My confusion is:
1) Is CI and sampling error related to sample size
2) Is sampling error determined by (1-alpha)? If so, 95% CI has a larger sampling error compared to 99%, because it has 5% and 1% sampling error respectively hey? But doesn't this involve a decrease, not increase in CI?

Also, would be great if someone could explain the difference between a 95% and 99% confidence interval.
 

hlsmith

Omega Contributor
#2
@Belle_Grace

all you need to do is look up some of the formulas for confidence intervals to answer your questions.

https://www.bing.com/images/search?view=detailV2&ccid=ZYMutTt3&id=4EBC9C1354B0B18AC601F4F421D904A6476D44DB&thid=OIP.ZYMutTt33Di4O_dmeFZIjQHaFj&mediaurl=http://www.wikihow.com/images/a/a7/Calculate-Confidence-Interval-Step-6-Version-4.jpg&exph=2400&expw=3200&q=confidence+interval+formula&simid=607994056698627121&selectedIndex=3&ajaxhist=0

CI = estimate +/- (alpha * SE)
depending on the distribution "n" is usually in the denominator of SE. So sample size is taken into account.

sampling error not determined by alpha, but a smaller alpha results in wider intervals given you want more confidence.