# Construction of confidence intervals and proper interpretation

Confidence Intervals: A confidence interval is simply a finite interval of score values on the dependent variable. Such an interval is constructed by adding a specific amount to the computed statistic (thereby obtaining the upper limit of the interval) and by the subtracting a specific amount from the statistic (thereby obtaining the lower limit of the interval). In addition to specifying the interval's upper and lower limits, researchers will always attach a percent to any interval that is constructed. The percentage value selected by the researcher will invariably be a high number, such as 90% or 95% of 99%.

In this paper present the Construction of confidence Intervals and the Proper Interpretation of confidence Intervals and show appropriate examples in the education environment.

© BrainMass Inc. brainmass.com October 10, 2019, 12:12 am ad1c9bdddfhttps://brainmass.com/statistics/confidence-interval/construction-confidence-intervals-proper-interpretation-283067

#### Solution Preview

Constructing Confidence Interval and its Interpretation

Why Confidence Interval:

In statistics, it is not always possible to work with the entire population. Besides cost and time issues sometimes it is not feasible to use the population. For example, if we are testing the quality of a product and the product is destroyed in the testing process, it is simply not possible to test all the items. In such cases, we make an estimate about the population parameters based on sample statistics. There are two types of estimates: point estimate and confidence interval. While point estimate is a single number estimate of the population parameter based on the sample statistics, the confidence interval are more popular, as instead of a single point it provides a finite interval of score values on the dependent variable.

How confident is the confidence interval?

When we are estimating the population parameter, we are never 100% confident that our estimate (s) are correct or not. Thus, we assign a probability figure to the confidence interval we build which indicates how confident we are. For example, a 99% ...

#### Solution Summary

The expert constructs the confidence intervals and proper interpretation.