There is no such thing. The standard error can be calculated for a sample of any size greater than 1.
When the sample size is small
2
The standard deviation would generally decrease because the large the sample size is, the more we know about the population, so we can be more exact in our measurements.
In general the mean of a truly random sample is not dependent on the size of a sample. By inference, then, so is the variance and the standard deviation.
The standard deviation of the population. the standard deviation of the population.
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)]
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.
No, it is not.
When the sample size is small
The standard error is the standard deviation divided by the square root of the sample size.
yes
2
No.
The standard deviation would generally decrease because the large the sample size is, the more we know about the population, so we can be more exact in our measurements.
A single observation cannot have a sample standard deviation.
In general the mean of a truly random sample is not dependent on the size of a sample. By inference, then, so is the variance and the standard deviation.
The formula for calculating uncertainty in a dataset using the standard deviation is to divide the standard deviation by the square root of the sample size.