Posted by matt on Friday, May 4, 2012 at 4:32pm.
You can try a proportional one-sample z-test for this one since this problem is using proportions.
Here's a few hints to get you started:
Null hypothesis:
Ho: p = .42 -->meaning: population proportion is equal to .42
Alternative hypothesis:
Ha: p > .42 -->meaning: population proportion is greater than .42
Using a formula for a proportional one-sample z-test with your data included, we have:
z = .44 - .42 -->test value (202/454 is approximately .44) minus population value (.42) divided by
√[(.42)(.58)/454] --> .58 represents 1-.42 and 454 is sample size.
Finish the calculation. Remember if the null is not rejected, then there is no difference. If you need to find the p-value for the test statistic, check a z-table. The p-value is the actual level of the test statistic.
I'll give you some background on Type I and Type II errors and let you take it from there.
Type I errors result when you reject the null and it's true. Type II errors result when you accept the null and it's false. You can reduce Type I errors by setting the alpha at a lower level, for example, from .05 to .01. However, when you do that, you increase the probability of making a Type II error. You would have to determine if the interested parties would be more concerned about Type I or Type II errors.
I hope these hints will help.
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