Answer:

And replacing we got:

And then the estimator for the standard error is given by:

Step-by-step explanation:
For this case we have the following dataset given:
20.05, 20.56, 20.72, and 20.43
We can assume that the distribution for the sample mean is given by:

And the standard error for this case would be:

And we can estimate the deviation with the sample deviation:

And replacing we got:

And then the estimator for the standard error is given by:
