First, consider the sample median, and then examine the confidence interval.
The median of the sample data is an estimate of the population median. Because the median is based on sample data and not on the entire population, it is unlikely that the sample median equals the population median. To better estimate the population median, use the confidence interval.
The confidence interval provides a range of likely values for the population median. 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 median. 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.
The 1sample Wilcoxon test does not always achieve the confidence level that you specify because the Wilcoxon statistic is discrete. Because of this, Minitab uses a normal approximation with a continuity correction to calculate the closest achievable confidence level.
 

In these results, the estimate of the population median for reaction time is 11.55. You can be approximately 94.8% confident that the population median is less than 12.5.
 
 

Use the graphs to look for symmetry and to identify potential outliers.
A distribution is symmetric when a vertical line can be drawn down the middle and the two sides will mirror each other. When the data are not symmetric, they are skewed to one side or the other.
If your data do not come from a symmetric distribution, use a 1Sample Sign.
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.