1. STANDARD ERROR OF SAMPLE
If many samples are taken from the same population, it is unlikely that they will all have characteristics identical with each other or with the population; their means will be different.
Sampling error is the difference between the sample mean and the population mean, due to the chance selection of individuals.
Sampling error reduces as the sample size increases.
Samples of >25 usually yield a normal sampling distribution of the mean.

2. CALCULATING THE STANDARD ERROR OF SAMPLE
Stage One: Draw several number of samples of equal size from a population, to create a sampling distribution.
Stage Two: Calculate the Standard Error (SE) of the mean:
SDs = standard deviation of the sample (a measure of dispersal around the mean)
N = the number in the sample

3. EXAMPLE OF STANDARD ERROR
If SDs = and N = 120
Then
The Standard Error (SE) is 1.27.