Hypothesis Testing for NCAA Champion Probability in Big East Conference

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To test the hypothesis that the probability of the NCAA champion being from the Big East is 20%, the null hypothesis (H0) states that the mean is equal to 20%, while the alternative hypothesis (HA) states it is not equal. This requires a two-tailed test due to the nature of the alternative hypothesis. The test statistic is calculated using the formula z = (sample mean - mu naught)/[(population deviation)(sample size)(1/2)], with a significance level of 0.05 divided by two for the two-tailed test. The corresponding z score will determine whether to reject or not reject the null hypothesis based on where the test statistic falls in relation to the critical values. Understanding these steps is crucial for conducting a proper hypothesis test in this context.
nautica
Using a .05 level of sign, test the hyp that the probability of the NCAA champion being from the big east is 20%

The actual ratio is 3/63

Where should I start?

thanks
nautica
 
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What do you mean by 'actual ratio'?
 
I'm a bit confused by your question but if I'm understanding it right:

You have two assumptions here: First you have a normal population or large sample and second, the population deviation is known.

State your null and alternative hypothesis.

H0: mu is equal to 20%
HA: mu is not equal to 20%

Because you are dealing with the alternative hypothesis that is not equal to mu naught, you will have a two tailed test with the Reject H0 in the two tails and the Do Not Reject H0 in the middle of the curve.

Now, you compute the value of the test statistic:

z = (sample mean - mu naught)/[(population deviation)(sample size)(1/2)]

I assume you are using a significance level of 5% but since you are dealing with a two tailed test, divide that area by two (in this case, 0.05/2). Essentially, this is the area you will be looking for in the table of z scores in the appendix of any Statistics book.

I don't have a book with me at the moment but look up the z score that corresonds to your significance level. If the test statistic falls in the Do Not Reject H0, obviously, don't reject H0. Likewise, if it falls in the reject zone, reject H0.

Hope this helps.
 
A bit of an embellishment to my earlier post:

The z score corresponding to your significance level is the numerical "mark" of where the Do not reject/ reject regions are.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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