An interval plot displays confidence intervals for the groups in your data. These confidence intervals (CI) are ranges of values that are likely to contain the true median of each population.
Because samples are random, two samples from a population are unlikely to yield identical confidence intervals. But, if you repeat your sample many times, a certain percentage of the resulting confidence intervals contain the unknown population parameter. The percentage of these confidence intervals that contain the parameter is the confidence level of the interval.
The confidence interval is composed of the following two parts:
- Point estimate
- The point estimate is the parameter that is calculated from the sample data. The confidence interval is centered around this value. For Mood's median test, the point estimate is the median estimate.
- Margin of error
- The margin of error defines the width of the confidence interval and is determined by the observed variability in the sample, the sample size, and the confidence level. To calculate the upper limit of the confidence interval, the margin of error is added to the point estimate. To calculate the lower limit of the confidence interval, the margin of error is subtracted from the point estimate.
Use the confidence interval to assess the estimate of the population median for each group.
For example, with a 95% confidence level, you can be 95% confident that the confidence interval contains the group 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.
In the interval plot, a temperature of 46 is associated with the heaviest weights. However, the test results are insignificant, which indicates that the observed differences are likely to be random error. You cannot determine from this graph whether any differences are statistically significant. To determine statistical significance, assess the p-value for the test.