Hypothesis Testing for NCAA Champion Probability in Big East Conference

If your computed z value falls within this region, you can reject or do not reject H0.In summary, using a .05 level of significance, you can test the hypothesis that the probability of the NCAA champion being from the Big East is 20%. The actual ratio is 3/63. The null hypothesis is that the probability is equal to 20%, and the alternative hypothesis is that it is not equal to 20%. By computing the test statistic and comparing it to the z score for a significance level of 0.05/2, you can determine whether to reject or do not reject the null hypothesis.
  • #1
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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  • #2
What do you mean by 'actual ratio'?
 
  • #3
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.
 
  • #4
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.
 

What is hypothesis testing for NCAA champion probability in Big East Conference?

Hypothesis testing for NCAA champion probability in Big East Conference is a statistical method used to determine the likelihood of a team winning the NCAA championship within the Big East Conference. It involves formulating a hypothesis, collecting data, and analyzing the results to determine the probability of the hypothesis being true.

Why is hypothesis testing important in this context?

Hypothesis testing is important in this context because it allows us to make informed decisions about the probability of a team winning the NCAA championship in the Big East Conference. It helps us understand the likelihood of a particular outcome and can guide us in making strategic decisions.

What are the steps involved in hypothesis testing for NCAA champion probability in Big East Conference?

The steps involved in hypothesis testing for NCAA champion probability in Big East Conference include formulating a null and alternative hypothesis, selecting a significance level, collecting and analyzing data, and interpreting the results to determine if the null hypothesis can be rejected or not.

What is a null hypothesis in this context?

In this context, the null hypothesis is the assumption that there is no difference in the probability of a team winning the NCAA championship in the Big East Conference compared to other conferences. It is the default position that we are trying to either reject or fail to reject based on the data collected.

How does hypothesis testing for NCAA champion probability in Big East Conference differ from other types of hypothesis testing?

Hypothesis testing for NCAA champion probability in Big East Conference is similar to other types of hypothesis testing in that it follows a similar process of formulating a hypothesis, collecting and analyzing data, and interpreting the results. However, it differs in terms of the specific variables and context being tested, as well as the significance level and data used for analysis.

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