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A systematic way to make a decision of whether to reject or not reject the **null hypothesis** is to compare the **p-value** and a **preset or
preconceived α (also called a "significance level")**. A preset α is the probability of a **Type I** **error** (rejecting the null hypothesis when
the null hypothesis is true). It may or may not be given to you at the beginning of the problem.

When you make a **decision** to reject or not reject H_{o}, do as follows:

- If α > p-value, reject
**H**_{o}. The results of the sample data are significant. There is sufficient evidence to conclude that**H**_{o}is an incorrect belief and that the**alternative hypothesis**,**H**_{a}, may be correct. - If α ≤ p-value, do not reject
**H**_{o}. The results of the sample data are not significant. There is not sufficient evidence to conclude that the alternative hypothesis,**H**_{a}, may be correct. - When you "do not reject
**H**_{o}", it does not mean that you should believe that**H**_{o}is true. It simply means that the sample data have**failed**to provide sufficient evidence to cast serious doubt about the truthfulness of**H**_{o}.

**Conclusion**: After you make your decision, write a thoughtful **conclusion** about the hypotheses in terms of the given problem.

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