Of course, we can't take 13.92 unints, so we bump the number up to 14. Thus, to achieve a power of .80, we collect data on fourteen units, assuming this sample size is feasible given our resources. As with the margin of error method, the ability of power analyses to select a sample size with the desired power is dependent on the accuracy of the standard deviation. Often, researchers undertake pilot studies to estimate the standard deviation. Without such studies, they make educated guesses of the standard deviation.
Researchers select the specific alternative hypothesis using scientific logic.
For example, we may want to detect a mean nicotine content of 1.5 mg because contents over that amount are especially addictive. When researchers are unsure of the alternative hypothesis they want to detect, they often perform pilot studies to get a rough sense of the deviations from the null hypothesis that can be expected. Then, they select a sample size for the larger study using an alternative hypothesis that is derived from the pilot study. Often, power analyses for sample size determination are required by granting agencies when researchers apply for grants for large studies. When I was a graduate student, I consulted for a mental health hospital that applied for grants to study children with ADHD and schizophrenics.