1. ESTIMATING APPROPRIATE SAMPLE SIZE

2. Determining sample Size z* σ √n ≤m

3. How Many monkeys? Researchers would like to estimate the mean cholesterol level ℳ of a particular variety of monkey that is often used in laboratory experiments. They would like their estimate to be within 1mg/dl of blood of the true value of ℳ at 95% confidence level. A previous study involving this variety of monkeys suggests that the sd of cholesterol level is about σ = 5mg/dl. Obtaining monkeys is time consuming and expensive so the researchers want to know the minimum number of monkeys they will need to generate a satisfactory estimate.

4. CI: 95% z* = 1.96 σ= 5mg/dl z* σ √n ≤ m 1.96 5 √n ≤1 (1.96) ≧ 1 (5) √n ≧ 9.8n ≧ 96.04 Researchers would need 97 monkeys to estimate the cholesterol levels to their satisfaction.

5. z* σ √n ≤ E E = maximum error of estimate

6. Finding the appropriate sample size (n) Mr. Delton asks Charlii to estimate the average age of the students in BHS. Mr. Delton is confident that Charlii will be able to find the minimum number of students he needs for his estimate to be reliable. Charlii would like to be 99% confident that the estimate should be accurate within 1 year. From a previous study, the standard deviation of the ages is known to be 3 years. Z* = 2.58 E = 1 year ∂ = 3 years

7. z* σ √n ≤ E Z* = 2.58 E = 1 year ∂ = 3 years n ≥ 59.9 Therefore, Charlii needs to have at least 60 students to be 99% confident that the estimate is within 1 year of the true mean age of the students in BHS

8. Your Turn! A restaurant owner wishes to find 99% confidence interval of the true mean cost of a dry martini. How large should the sample be if she wishes to be accurate within $0.10? A previous study showed that the population standard deviation of the the price was $0.12.

9. Facts about the margin of error z* gets smaller. The trade-off: to obtain smaller margin of error from the same data, you must be willing to accept lower confidence. σ g ets smaller: its easier to pin down ℳ when σ is small n gets bigger: increasing the sample size reduces the margin of error. To cut the margin of error in half, you must take four times as many observations from the population.