what is the risk of having too small a sample to measure beta?
925437
Any information you gather may not reflect a real-world situation.
446411
Usually when  you pull a sample, you know the size of the sample you pulled.  Otherwise, how would you calculate any of the test statistics much less the margin of error?
710109
in small quanitys it is fine but not in large quanitys. Like 1ml is bad
1156399
This is a good question even for a statistician.\nThe problem is your set is small and I do not know what the deviation between samples is, however the repeatability and reproducibility information can bind the window \nthrough which the third value will fall through or perhaps even remain in.  \n\nLet me know.
697476
(a) Adding another observation makes the test more significance, not less significant.    That would make the p-value smaller, and it was already smaller than 1% since you rejected the null originally.  You would still reject the null at the 1% level.\n\n(b) If a test is rejected at the 1% level, the p-value was less than 1%.  Since that is the case, it would also be less than 5%, which means you would reject the null at the 5% level.
129007
Look for stocks with a BETA of 2 - 3.  That means if the S & P 500 goes up 5 percent this next few months, a stock with a Beta of 2 should go up 10 percent and a Beta of 3 should go up 15 percent.  Be careful though, if the market goes down 5 percent your stock should go down by the same 10 and 15 percent repectivley.
327573
Let's see...it's desireable because it's likely to be a lot different than your starting material and your derivatizing agent - this gives you confidence that you actually made a derivative and are not just measuring the m.p. of either your unreacted agent or your starting material.  We used this a lot when I took a class called the Analysis of Organic Unkowns.  No instruments were allowed.
790907
b is the most convincing.\n\n90 is the value farthest from the mean, and a small sample variance indicates that it's unlikely that you just somehow ended up with an unusually high (or low) population.
660150
That would not be possible without the sample values or some other information.  You can make some statements about possible sample means.  For example, since the standard error of the sample mean would be 0.29/sqrt(25) = 0.058, then\n\nThe probability that the sample mean will be between 3.02-0.058 and 3.02+0.058 is 68%.\nThe probability that the sample mean will be between 3.02-0.116 and 3.02-0.116 is 95%.\n\nand so on.
764277
during and distructive test, danger exists. knowlege of the equiptment and proceedures are a must. if the exact failure point is to be measured, then at yield the test should be slowed. It will also minimise flying samples.
871703
Sorry, if the result can be greater than 1 then you ain't estimating a proportion.  For proportions each unit in the sample gives you either 0 or 1 as a result.\n\nYou need to swith to the general case where you calculate the standard deviation from the sum of squared differences from the mean, etc. PQ/n or whatever cannot be applied.
374562
I think that you would need at least 2 values in each cell so that the within groups SD can be estimated, so if each of the two ways has two factors then you would need a sample of at least 8, so that there is two for each cell.  Could be wrong though.
166114
Try seraching MS site to register for a Beta evaluation program :-|
610087
The lab values you have given are not that far off from normal.  Don't jump the gun thinking you have something majorly wrong, talk to your doc!!!
580641
If the data is roughly symmetric with no outliers (values that are unlike the rest of the data), then yes, SD should be fine.  However, if the data is skewed (having a tail to the left or right) or there are outliers, then I would use IQR to describe the spread of the data instead.
121268
It is used to compare two small sets of quantitative data when samples are collected independently of one another.
685395
Depends on the situation.\n\nFor example, you want to decide whether a batch of products is defective.  If your test come out to be significant but actually not (type I error), you have thrown away a perfectly fine batch of products.\n\nOn the other hand, for the same batch of products, if your test come out to be not significant but actually the batch is defective (type II error), then you have accepted a bad lot of products. \n\nSo in summary, type I error wastes money, type II error saves money but consumers don't like it.  Use similar analysis in your case.