**Quality Quiz **from Professor Cleary
**Congratulations:**
"A" is correct.
The way to achieve this level of confidence is to select the proper sample size. Two factors are critical in sampling: error and reliability. Error (*e*) is the amount one is willing to miss by (for example, Quinn’s plus and minus 2 percent). Reliability represents the desired level of confidence (for example, 95 percent).
With error and reliability understood, we can address the sample size that is necessary to achieve the desired error and reliability levels. In Quinn’s case, his statistics book indicates that the following formula, derived from the central limit theorem for proportional data, which applies to calculating sample size for proportional data:
In Quinn’s example, the following apply:
= .5 (Use .5 if no accurate estimate is available, since .5 will generate the highest *n.*)
The question arises: What does this mean?
If Quinn were to take a sample of 2400, he could be 95 percent confident that the sample proportion would lie within 2 percent of the true proportion.
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