Friday

April 18, 2014

April 18, 2014

Posted by **matt** on Friday, May 4, 2012 at 4:32pm.

1) test an appropriate hypothesis and state your conclusion.

2) clearly explain what a Type-I error is in this context and give a possible consequence of making a type-I error.

3) clearly explain what a type II error is in this context and give a possible consequence of making a type-II error.

4) clealry explain, in context, what your P-value means

- statistics -
**MathGuru**, Friday, May 4, 2012 at 6:11pmYou 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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