Test about proportions  (Page 3/3)

In general, without changing the sample size or the type of the test of the hypothesis, a decrease in $\alpha$ causes an increase in $\beta$ , and a decrease in $\beta$ causes an increase in $\alpha$ . Both probabilities $\alpha$ and $\beta$ of the two types of errors can be decreased only by increasing the sample size or, in some way, constructing a better test of the hypothesis.

Example

If n =100 and we desire a test with significance level $\alpha$ =0.05, then $\alpha =P\left(\overline{X}\ge c;\mu =60\right)=0.05$ means, since $\overline{X}$ is $\text{N(}\mu \text{,100/100=1)}$ ,

$$P\left(\frac{\overline{X}-60}{1}\ge \frac{c-60}{1};\mu =60\right)=0.05$$ and $c-60=1.645$ . Thus c =61.645. The power function is

$$K\left(\mu \right)=P\left(\overline{X}\ge 61.645;\mu \right)=P\left(\frac{\overline{X}-\mu }{1}\ge \frac{61.645-\mu }{1};\mu \right)=1-\Phi \left(61.645-\mu \right).$$

In particular, this means that $\beta$ at $\mu$ =65 is $$=1-K\left(\mu \right)=\Phi \left(61.645-65\right)=\Phi \left(-3.355\right)\approx 0;$$ so, with n =100, both $\alpha$ and $\beta$ have decreased from their respective original values of 0.1587 and 0.0668 when n =25. Rather than guess at the value of n , an ideal power function determines the sample size. Let us use a critical region of the form $\overline{x}\ge c$ . Further, suppose that we want $\alpha$ =0.025 and, when $\mu$ =65, $\beta$ =0.05. Thus, since $\overline{X}$ is $\text{N(}\mu \text{,100/n)}$ ,

$$0.025=P\left(\overline{X}\ge c;\mu =60\right)=1-\Phi \left(\frac{c-60}{10/\sqrt{n}}\right)$$ and $$0.05=1-P\left(\overline{X}\ge c;\mu =65\right)=\Phi \left(\frac{c-65}{10/\sqrt{n}}\right).$$

That is, $\frac{c-60}{10/\sqrt{n}}=1.96$ and $\frac{c-65}{10/\sqrt{n}}=-1.645$ .

Solving these equations simultaneously for c and $10/\sqrt{n}$ , we obtain $$c=60+1.96\frac{5}{3.605}=62.718;$$ $$\frac{10}{\sqrt{n}}=\frac{5}{3.605}.$$

Thus, $\sqrt{n}=7.21$ and $n=51.98$ . Since n must be an integer, we would use n =52 and obtain $\alpha$ =0.025 and $\beta$ =0.05, approximately.

For a number of years there has been another value associated with a statistical test, and most statistical computer programs automatically print this out; it is called the probability value or, for brevity, p -value . The p -value associated with a test is the probability that we obtain the observed value of the test statistic or a value that is more extreme in the direction of the alternative hypothesis, calculated when ${\text{H}}_{\text{0}}$ is true. Rather than select the critical region ahead of time, the p -value of a test can be reported and the reader then makes a decision.

Say we are testing ${\text{H}}_{\text{0}}\text{: }\mu \text{=60 }$ against ${\text{H}}_{\text{1}}\text{: }\mu \text{>60}$ with a sample mean $\overline{X}$ based on n =52 observations. Suppose that we obtain the observed sample mean of $\overline{x}=62.75$ . If we compute the probability of obtaining an $\overline{x}$ of that value of 62.75 or greater when $\mu$ =60, then we obtain the p -value associated with $\overline{x}=62.75$ . That is,

$$\begin{array}{l}p-value=P\left(\overline{X}\ge 62.75;\mu =60\right)=P\left(\frac{\overline{X}-60}{10/\sqrt{52}}\ge \frac{62.75-60}{10/\sqrt{52}};\mu =60\right)\\ =1-\Phi \left(\frac{62.75-60}{10/\sqrt{52}}\right)=1-\Phi \left(1.983\right)=\mathrm{0.0237.}\end{array}$$

If this p -value is small, we tend to reject the hypothesis ${\text{H}}_{\text{0}}\text{: }\mu \text{=60 }$ . For example, rejection of ${\text{H}}_{\text{0}}\text{: }\mu \text{=60 }$ if the p -value is less than or equal to 0.025 is exactly the same as rejection if $\overline{x}=62.718$ .That is, $\overline{x}=62.718$ has a p -value of 0.025. To help keep the definition of p -value in mind, we note that it can be thought of as that tail-end probability , under ${\text{H}}_{\text{0}}$ , of the distribution of the statistic, here $\overline{X}$ , beyond the observed value of the statistic. See Figure 1 for the p -value associated with $\overline{x}=\mathrm{62.75.}$