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A decade-old study showed that the proportion of high school seniors who felt that "getting rich" is an important personal was 74%. Suppose we have reason to believe that this proportion has changed, and we want to make a hypothesis test to see if our faith can be supported. The null hypothesis and the alternative hypothesis that we would use for this test.
H 0:
H 1:

a�e H: p = 0.74 vs. H a��: p a�� 0.74

Here is some information that should help you understand what type of problem in the future.

Let us assume that a trial against the null hypothesis. The data are evidence against average. You assume the mean is true and try to prove that this is not true. After finding the test statistic and p-value, if the p-value less than or equal to the level of significance of the test, we reject the null hypothesis and conclude the alternative hypothesis is true. If the p-value is greater than a level of significance, then we can not reject the null hypothesis and conclude it is plausible. Note that we can not conclude the null hypothesis is true, just that it is plausible.

If statement question asks whether there is a difference between statistics and a value, then you have a two-tailed test, the null hypothesis, for example, would be μ = D vs the alternative hypothesis μ a�� d

if the question ask to test an inequality you make sure your results will be worth it. example. say you have a steel bar that will be used in a construction project. If the bar can support a load of 100,000 pounds per square inch, then you'll use the bar, if it does so you may not use the bar.

if the null hypothesis is μ a�� 100,000 vs the alternate μ <100,000 then will be a test of significance. In this case, if you reject the null hypothesis, you conclude that the alternative hypothesis is true and the average load of the bar can support is less than 100,000 pounds per square inch and you will not be able to use bar. However, if you fail to reject the null then you conclude it is plausible the mean is greater than or equal to 100,000. We can never conclude that the null hypothesis is true. Therefore, you should not use the bar because you have no proof that the average strength is quite high.

if the null hypothesis is μ a�� 100,000 against the alternate μ> 100,000 and you reject the null then you conclude the alternate is true and the bar is strong enough, if you are unable to reject it is plausible the bar is not strong enough, so you do not use it. In this case, you have a significant result.

Whenever you set the hypothesis test you need to consider whether or not the results will be significant.