Answer:
We would have to take a sample of 62 to achieve this result.
Step-by-step explanation:
Confidence level of 95%.
We have that to find our
level, that is the subtraction of 1 by the confidence interval divided by 2. So:

Now, we have to find z in the Ztable as such z has a pvalue of
.
That is z with a pvalue of
, so Z = 1.96.
Now, find the margin of error M as such

In which
is the standard deviation of the population and n is the size of the sample.
Assume that the standard deviation in the amount of caffeine in 8 ounces of decaf coffee is known to be 2 mg.
This means that 
If we wanted to estimate the true mean amount of caffeine in 8 ounce cups of decaf coffee to within /- 0.5 mg, how large a sample would we have to take to achieve this result?
We would need a sample of n.
n is found when
. So



Dividing both sides by 0.5



Rounding up
We would have to take a sample of 62 to achieve this result.