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Standard error of the sample mean is calculated dividing the the sample estimate of population standard deviation ("sample standard deviation") by the square root of sample size.
A single observation cannot have a sample standard deviation.
The standard error should decrease as the sample size increases. For larger samples, the standard error is inversely proportional to the square root of the sample size.The standard error should decrease as the sample size increases. For larger samples, the standard error is inversely proportional to the square root of the sample size.The standard error should decrease as the sample size increases. For larger samples, the standard error is inversely proportional to the square root of the sample size.The standard error should decrease as the sample size increases. For larger samples, the standard error is inversely proportional to the square root of the sample size.
If the population standard deviation is sigma, then the estimate for the sample standard error for a sample of size n, is s = sigma*sqrt[n/(n-1)]
The sample standard deviation (s) divided by the square root of the number of observations in the sample (n).
sample area/standard area*standard weight/sample weight*standard purity/100*100
No, it is not.
In external standard method we run a standard for the substance we are looking for in our sample and than run our sample in the same conditions. we will obtain peaks for the sample and standards and can correlate the sample with the standards and can determine the concentration of the same in our sample. for more details contact vani2007
The sample standard error.
the sample mean is used to derive the significance level.
There is no such thing. The standard error can be calculated for a sample of any size greater than 1.