The confidence interval provides a range of likely values for the population difference. 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 difference. The percentage of these confidence intervals or bounds that contain the difference 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 difference.
An upper bound defines a value that the population difference is likely to be less than. A lower bound defines a value that the population difference 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 difference in proportions in summer employment for male and female students is 0 approximately 0.099. You can be 95% confident that the ratio of population standard deviations is between approximately 0.06 and 0.13.
The difference is the unknown difference between the population proportions that you want to estimate. Minitab indicates which population proportion is subtracted from the other.
The difference is the difference between the proportions of the two samples.
Because this value is based on sample data and not on the entire population, it is unlikely that the sample difference equals the population difference. To better estimate the population difference, use the confidence interval for the difference.
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 difference with a reference line that indicates the hypothesized difference of 0.
The confidence interval provides a range of likely values for the population difference. 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 difference. The percentage of these confidence intervals or bounds that contain the difference 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 difference.
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 these results, the null hypothesis is that the population difference is equal to 0. The alternative hypothesis is that the difference is not equal to 0.
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 difference in population proportions is statistically significant.
Minitab uses the normal approximation method and Fisher's exact method to calculate the pvalues for the 2 proportions test. If the number of events and the number of nonevents is at least 5 in both samples, use the smaller of the two pvalues. If either the number of events or the number of nonevents is less than 5 in either sample, the normal approximation method may be inaccurate. Fisher's exact method is valid for all samples, but tends to be conservative. A conservative pvalue understates the evidence against the null hypothesis.
The pooled estimate of the proportion is a weighted average of the proportions from the two samples. Minitab uses this value to calculate the pvalue for each test. Some introductory statistics textbooks mention the possibility of using a pooled estimate of the proportion because it is easier to calculate manually and saves time. The 2 Proportions test includes this option to help students replicate their manual calculations in Minitab.
To have Minitab display this value in the output, on the Options tab, select Use the pooled estimate of the proportion for Test method.
The sample proportion equals the number of events divided by the sample size (N).
The proportion of each sample is an estimate of the population proportion of each sample.
The Zvalue is a test statistic for Ztests that measures the difference between an observed statistic and its hypothesized population parameter in units of standard error.
You can compare the Zvalue to critical values of the standard normal distribution to determine whether to reject the null hypothesis. However, using the pvalue of the test to make the same determination is usually more practical and convenient.
To determine whether to reject the null hypothesis, compare the Zvalue to your critical value. The critical value is Z_{1α/2} for a two–sided test and Z_{1α} for a one–sided test. For a twosided test, if the absolute value of the Zvalue is greater than the critical value, you reject the null hypothesis. If the absolute value of the Zvalue is less than the critical value, you fail to reject the null hypothesis. You can calculate the critical value in Minitab or find the critical value from a standard normal table in most statistics books. For more information, go to Using the inverse cumulative distribution function (ICDF) and click "Use the ICDF to calculate critical values".