But here, they have considered a $t$-test. But the sample size is large enough to do a $Z$-test.
Again, the link doesn't consider 'upper tail'. So their conclusion is in contradiction with mine.
Which one is correct?
$\begingroup$ The test is one-sided, you need to check the rejection region of your approach. $\endgroup$
Commented Aug 10, 2013 at 12:55$\begingroup$ If the population standard deviation is known, then you can use a $z$-test. As far as I understand your setup, you assume to know the population standard deviation to be $500$. If you estimate the population standard deviation by the sample standard deviation, a $t$-test is normally used. In large samples, the difference between the $z$-test and $t$-test are negligible. The critical $t$ value on the linked page is the $0.95$ quantile of the $t$-distribution with $34$ degrees of freedom. The $0.95$ quantile of the standard normal is $1.644$, not $1.96$ (because you have a one-sided test). $\endgroup$
Commented Aug 10, 2013 at 12:57$\begingroup$ @COOLSerdash Can you please give me reference on one-sided test and two-sided test. My main problem is to compute the value based on one-sided test and two-sided test. $\endgroup$
Commented Aug 10, 2013 at 13:28 $\begingroup$ For a two sided test I believe you just use alpha/2 as alpha. $\endgroup$ Commented Aug 10, 2013 at 13:41$\begingroup$ @JustinBozonier A two sided test, statistic $z=2.42$. $p-$value$=2*0,0078$. Can you please explain why did we multiply $2$ instead of dividing? $\endgroup$
Commented Aug 10, 2013 at 13:48"They randomly select 40 claims. "
First, you only have 40 samples. (not 40 samples. One sample with 40 cases). That's not enough to do a Z-test. You are in solid T-test territory as is your example.
"They are concerned that the true mean is actually higher than this. " So this will be a one sided test. Looking up the test statistic for a one sided t-test with 39 degrees of freedom (from here http://www.sjsu.edu/faculty/gerstman/StatPrimer/t-table.pdf)
Also according to that table you don't have a z statistic until you've had over 1,000 samples.
The closest I can come is 40 so the statistic would be 1.684. Your null and alternative hypotheses look good to me
Using the one sample T-test referenced here (the same as your z-statistic): http://en.wikipedia.org/wiki/Student's_t-test
Which is the same value as you. Since $t > \alpha$ we reject the null hypothesis.
I think the main issue here is using a one sided test vs two sided and understanding you are not able to use a z-test with so few samples.