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Oct 28, 2019 at 17:59 vote accept profPlum
Oct 28, 2019 at 17:59 comment added profPlum works! cool I still don't understand why one-sided t.test uses norm quantiles but whatever I'll give it to you because you answered most of my question
Oct 23, 2019 at 0:30 comment added Dave @profPlum What happens if you run my code?
Oct 22, 2019 at 21:03 comment added profPlum I should mention however that I never get this narrower interval all in one piece, since they are one-sided tests one side is always infinity. If you look at the comments you'll see what I get exactly.
Oct 22, 2019 at 20:59 comment added profPlum I did and I just reran it to double check, the verbatim R code is in the first code-box of my post (not counting the inline one).
Oct 22, 2019 at 20:37 comment added Dave @profPlum Please post your verbatim R code. I do not get the confidence interval that you get.
Oct 22, 2019 at 20:37 history edited Dave CC BY-SA 4.0
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Oct 22, 2019 at 20:12 comment added profPlum Thanks that is helpful to know. But no those are the numbers that I get for one-sided intervals (please recheck my post)! That's my point; I don't understand why I get those for one-sided intervals (apparently using norm-quantile) but the wider one (using t-quantile) for two-sided. And to reiterate these are both from the t.test() function.
Oct 22, 2019 at 19:29 comment added Dave You use the t-test to account for the fact that you're estimating the variance. For the one-sided intervals, I am not getting the numbers that you are getting. My confidence interval is narrower: $( 28.69617 ,38.50383 )$.
Oct 22, 2019 at 18:47 comment added profPlum So then is only the t-quantile CI correct? Also could you please address why t.test's finite one-sided CI boundaries appear to also use norm-quantiles instead of t-quantiles?
Oct 22, 2019 at 18:24 history answered Dave CC BY-SA 4.0