When conducting a hypothesis test with a binomial distribution (sometimes called a Binomial Test), there are three ways to calculate the P-value (with additional variations possible).  The only exact calculation is to use the binomial probability distribution.  The other methods are approximations using the standardized normal distribution (when certain criteria have been achieved).  Of these two methods, one can use the sample counts or one can use the sample proportions.  Furthermore, it is possible in both of these approximating cases to apply a continuity correction to account for the use of a continuous distribution to approximate a discrete distribution.

This problem introduces the method to obtain an exact P-value using the binomial distribution.  Also, this method uses Excel to obtain the answers.

For this demonstration problem, we will test a hypothesis that a population proportion has increased since the last time it was measured.  Previously, the population proportion was measured at 20%.  For the current analysis, a sample of $\displaystyle {n}={100}$ randomly chosen subjects was obtained, and 25 of those demonstrated the observation of interest (i.e., a success).

To start, we clearly construct the hypotheses for this problem.  Because the researcher suggested the proportion has increased, this would suggest a one-tailed test (as can be seen in the choice of $\displaystyle {H}_{{a}}$):
      $\displaystyle {H}_{{o}}:{p}={0.2}$
      $\displaystyle {H}_{{a}}:{p}>{0.2}$

Using the binomial distribution, the test statistic from the sample would simply be the sample count (the number of successful observations):
      $\displaystyle {k}={25}$

The P-value for this scenario would be observing this count or one more extreme.  With the alternative hypothesis suggesting that values at or below 20% would be unsurprising, this would suggest that the observed count or larger would constitute the potentially extreme responses.  Thus, the P-value would be:
      $\displaystyle {P}{\left({X}\ge{25}{\mid}{p}={0.2},{n}={100}\right)}$

Using Excel, this value can be calculated exactly: =1-BINOMDIST(24,100,20%,TRUE) which should return a P-value of 0.1314.  Thus, with a traditional significance level of either $\displaystyle \alpha={0.05}$ or $\displaystyle \alpha={0.01}$, this P-value would result in failing to reject the null hypothesis.  Thus, there is not enough sample evidence to support the claim that the population proportion has increased.

Note:  For a two-tailed hypothesis test, the calculation can become a bit tricky.  In this case, it is necessary to take the counts at and above the sample count, but it is also necessary to determine comparable counts below the hypothesized population count.  This would be obtained using the formula $\displaystyle {n}\cdot{p}-{\left({k}-{n}\cdot{p}\right)}$ This value is the count below the hypothesized mean count by the same distance as the sample count was above the mean count.  For this demonstration example, this value would be 15.  To obtain the P-value from Excel, you would use the following formula: =BINOMDIST(15,100,20%,TRUE)+1-BINOMDIST(24,100,20%,TRUE) Though, it is recommended that the two-tailed test only be used with one of the approximation methods as the calculations are less cumbersome.

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Exercise Problem
In 1975, only 7% of the students in the city school district were classified as being learning disabled.  A school psychologist suspects that the proportion of learning-disabled children has increased dramatically over the years.  To demonstrate this point, a random sample of $\displaystyle {n}={400}$ students is selected.  In this sample there are 41 students who have been identified as learning-disabled.  You will use this information to determine if the sample indicates a change in the proportion of learning-disabled students at a 0.05 level of significance.

What is the hypothesized (assumed constant) population proportion for this test?
$\displaystyle {p}=$
(Report answer as a decimal accurate to 2 decimal places.  Do not report using the percent symbol.)

Based on the researcher's understanding of the situation, how many tails would this hypothesis test have?

Choose the correct pair of hypotheses for this situation:

(A)	(B)	(C)
$\displaystyle {H}_{{0}}:{p}={0.07}$ $\displaystyle {H}_{{a}}:{p}<{0.07}$	$\displaystyle {H}_{{0}}:{p}={0.07}$ $\displaystyle {H}_{{a}}:{p}\ne{0.07}$	$\displaystyle {H}_{{0}}:{p}={0.07}$ $\displaystyle {H}_{{a}}:{p}>{0.07}$
(D)	(E)	(F)
$\displaystyle {H}_{{0}}:{p}={0.103}$ $\displaystyle {H}_{{a}}:{p}<{0.103}$	$\displaystyle {H}_{{0}}:{p}={0.103}$ $\displaystyle {H}_{{a}}:{p}\ne{0.103}$	$\displaystyle {H}_{{0}}:{p}={0.103}$ $\displaystyle {H}_{{a}}:{p}>{0.103}$

The test statistic for this analysis is the sample count (i.e., the number of observed successes).  What is this value?
$\displaystyle {k}=$

With these hypotheses, the p-value for this test is (assuming $\displaystyle {H}_{{o}}$ is true) the probability of observing...

You are now ready to calculate the P-value for this sample. Be sure to use the (cumulative) binomial distribution to obtain an exact P-value.  (Do not use the normal distribution as an approximation for the binomial distribution for this particular problem.)
P-value =
(Report answer as a decimal accurate to 4 decimal places.)

This P-value (and test statistic) leads to a decision to...

As such, the final conclusion is that...