Worked solution
Choose a sample size from an exact normal mean distribution
The life, in hours, of a randomly chosen light bulb is normally distributed with mean 1100 and standard deviation 70. How large a sample is required so that the probability that its mean life exceeds 1120 is not more than 5%? Do you need the central limit theorem?
CJC · Discussion · Question 6
Answer
Least sample size ; CLT is not needed.
Method 1: solve the upper-tail inequality
For independent lifetimes , the mean has exact distribution . Thus requires . Hence and the least integer is 34.
Method 2: use an integer table
The source enters normalcdf(1120,10^99,1100,√(70²/X)), with lower 1120, upper , mean 1100 and standard deviation . Its printed table is:
| Probability | |
|---|---|
| 33 | 0.0504 |
| 34 | 0.0479 |
| 35 | 0.0455 |
At 33 the probability exceeds 0.05; at 34 it is about 0.0478581, so 34 is the first acceptable integer. The screenshot’s surrounding values are:
Source screen values:
| Probability | |
|---|---|
| 30 | 0.0588 |
| 31 | 0.0558 |
| 32 | 0.0530 |
| 33 | 0.0504 |
| 34 | 0.0479 |
| 35 | 0.0455 |
| 36 | 0.0432 |
| 37 | 0.0411 |
| 38 | 0.0391 |
| 39 | 0.0372 |
| 40 | 0.0354 |
Explain why CLT is unnecessary
The population is explicitly normal, so its independent sample mean is exactly normal for every positive sample size. No CLT approximation is needed.
Common mistakes
The standard deviation of the mean is 70/√n. Use the upper tail and do not reverse the sample-size inequality.
Solution notes
No marks are printed for this question; none are invented.