The confidence interval provides a range of likely values for the population proportion. Because samples are random, two samples from a population are unlikely to yield identical confidence intervals. But, if you repeated your sample many times, a certain percentage of the resulting confidence intervals or bounds would contain the unknown population proportion. The percentage of these confidence intervals or bounds that contain the proportion is the confidence level of the interval. For example, a 95% confidence level indicates that if you take 100 random samples from the population, you could expect approximately 95 of the samples to produce intervals that contain the population proportion.
An upper bound defines a value that the population proportion is likely to be less than. A lower bound defines a value that the population proportion is likely to be greater than.
The confidence interval helps you assess the practical significance of your results. Use your specialized knowledge to determine whether the confidence interval includes values that have practical significance for your situation. If the interval is too wide to be useful, consider increasing your sample size. For more information, go to Ways to get a more precise confidence interval.
 

In these results, the estimate of the population proportion for households that made a purchase is 0.087. You can be 95% confident that the population proportion is between approximately 0.07 and 0.106.
The number of events (also called the number of successes) is the number of observations that have a specific characteristic within a sample. The Method table displays the value in the sample that represents the event.
Minitab uses the number of events to calculate the sample proportion, which is an estimate of the population proportion.
The interval plot shows the confidence interval for the sample proportion with a reference line that indicates the hypothesized proportion.
The confidence interval provides a range of likely values for the population proportion. Because samples are random, two samples from a population are unlikely to yield identical confidence intervals. But, if you repeated your sample many times, a certain percentage of the resulting confidence intervals or bounds would contain the unknown population proportion. The percentage of these confidence intervals or bounds that contain the proportion is the confidence level of the interval. For example, a 95% confidence level indicates that if you take 100 random samples from the population, you could expect approximately 95 of the samples to produce intervals that contain the population proportion.
The confidence interval helps you assess the practical significance of your results. Use your specialized knowledge to determine whether the confidence interval includes values that have practical significance for your situation. If the interval is too wide to be useful, consider increasing your sample size. For more information, go to Ways to get a more precise confidence interval.
The sample size (N) is the total number of observations in the sample.
The sample size affects the confidence interval and the power of the test.
Usually, a larger sample size results in a narrower confidence interval. A larger sample size also gives the test more power to detect a difference. For more information, go to What is power?.
In the output, the null and alternative hypotheses help you to verify that you entered the correct value for the hypothesized proportion.
 
 

In these results, the null hypothesis is that the population proportion is equal to 0.065. The alternative hypothesis is that the proportion is not equal to 0.065.
The pvalue is a probability that measures the evidence against the null hypothesis. A smaller pvalue provides stronger evidence against the null hypothesis.
Use the pvalue to determine whether the population proportion is statistically different from the hypothesized proportion.
The sample proportion equals the number of events divided by the sample size (N).
The sample proportion is an estimate of the population proportion.
Because the proportion is based on sample data and not on the entire population, it is unlikely that the sample proportion equals the population proportion. To better estimate the population proportion, use the confidence interval.