The hypotheses. In Example 1, we want to test the hypotheses
Here, p is the proportion of the population of all coffee drinkers who prefer fresh coffee to instant coffee.
The sampling distribution. If the null hypothesis is true, so that p = 0.5, we saw in Example 1 that follows a Normal distribution with mean 0.5 and standard deviation, or standard error, 0.0707.
The data. A sample of 50 people found that 36 preferred fresh coffee. The sample proportion is .
The P-value. The alternative hypothesis is one-sided on the high side. So, the P-value is the probability of getting an outcome at least as large as 0.72. Figure 22.1 displays this probability as an area under the Normal sampling distribution curve. To find any Normal curve probability, move to the standard scale. When we convert a sample statistic to a standard score when conducting a statistical test of significance, the standard score is commonly referred to as a test statistic. The test statistic for the outcome is
Table B says that standard score 3.1 is the 99.9 percentile of a Normal distribution. That is, the area under a Normal curve to the left of 3.1 (in the standard scale) is 0.999. The area to the right is therefore 0.001, and that is our P-value.
The conclusion. The small P-value means that these data provide very strong evidence that a majority of the population prefers fresh coffee.