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?.
The median is the midpoint of the data set. This midpoint value is the point at which half the observations are above the value and half the observations are below the value. The median is determined by ranking the observations and finding the observation that are at the number [N + 1] / 2 in the ranked order. If the number of observations are even, then the median is the average value of the observations that are ranked at numbers N / 2 and [N / 2] + 1.
The median of each sample is an estimate of the population median of each sample.
The difference is the difference between the medians 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 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.
 

In these results, the point estimate of the population median for the difference in the number of months that paint persists on two highways is –1.85. You can be 95.5% confident that the difference between the population medians is between –3.0 and –0.9.
The MannWhitney test does not always achieve the confidence interval that you specify because the MannWhitney statistic (W) is discrete. Minitab calculates the closest achievable confidence level.
The achieved confidence indicates how likely it is that the population difference is contained in the confidence 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.
 
 

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 MannWhitney statistic (WValue) is the sum of the ranks of the first sample.
Minitab uses the MannWhitney statistic to calculate the pvalue, which is a probability that measures the evidence against the null hypothesis.
Because the interpretation of the MannWhitney statistic depends on the sample size, use the pvalue to make a decision about the test. The pvalue has the same meaning for any sample size.
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 medians is statistically significant.
A tie occurs when the same value is in both samples. If your data has ties, Minitab displays a pvalue that is adjusted for ties and a pvalue that is not adjusted. The adjusted pvalue is usually more accurate than the unadjusted pvalue. However, the unadjusted pvalue is the more conservative estimate because it is always greater than the adjusted pvalue for a specific pair of samples.
A boxplot provides a graphical summary of the distribution of each sample. The boxplot makes it easy to compare the shape, the central tendency, and the variability of the samples.
Use a boxplot to identify any potential outliers. Boxplots are best when the sample size is greater than 20.
Outliers, which are data values that are far away from other data values, can strongly affect the results of your analysis. Often, outliers are easiest to identify on a boxplot.
Try to identify the cause of any outliers. Correct any data–entry errors or measurement errors. Consider removing data values for abnormal, onetime events (also called special causes). Then, repeat the analysis. For more information, go to Identifying outliers.
An individual value plot displays the individual values in each sample. An individual value plot makes it easy to compare the samples. Each circle represents one observation. An individual value plot is especially useful when you have relatively few observations and when you also need to assess the effect of each observation.
Use an individual value plot to identify any potential outliers. Individual value plots are best when the sample size is less than 50.
Outliers, which are data values that are far away from other data values, can strongly affect the results of your analysis. Often, outliers are easiest to identify on a boxplot.
Try to identify the cause of any outliers. Correct any data–entry errors or measurement errors. Consider removing data values for abnormal, onetime events (also called special causes). Then, repeat the analysis. For more information, go to Identifying outliers.