These confidence intervals (CI) are ranges of values that are likely to contain the true values of the Best Linear Unbiased Prediction (BLUP) for the random terms in the model.
Because samples are random, two samples from a population are unlikely to yield identical confidence intervals. However, if you take many random samples, 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
- This single value estimates a population parameter by using your sample data. The confidence interval is centered around the point 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 specific level effect of a random term on the response. An interval that does not contain 0 indicates a statistically significant effect. If the interval is strictly greater than 0, the specific level has a positive effect on the response. An interval that is strictly less than 0 indicates a negative effect on the response. An interval that contains 0 does not support a significant level effect of the random term on the response.