A confidence interval describes a range of values that are likely to include the true value for a population. The upper and lower confidence limits are the two numbers that make up the range of the interval. Confidence intervals do not provide certain answers, they are an estimate based on a sample.
Commonly either a 95% or 99% confidence interval is used in experiments, which equates to either a 5% or 1% significance level (p-value). The interval describes all values for which we cannot reject the null hypothesis at the given significance level. If a 95% confidence interval has been calculated and your experiment provides a value inside the interval, you can state that your conclusions would be right 95% of the time.
Here is an example of how to find a confidence interval for a population mean. In this example:
n (number of observations) = 70
The sample standard mean (x-bar) = 48
The sample standard deviation = 15
We want to find a 99% confidence interval
The critical value for a z-test at 99% significance = 2.58
Note that the calculations are the same for a 95% confidence interval, apart from the critical value being 1.96 rather than 2.58. For 95% confidence, the interval is (44.49, 51.51).
Interpreting a confidence interval
Taking a random sample and applying a 95% confidence interval, the interval will give all values for the population mean that would not be rejected at the 5% significance level. Therefore on about 95% of occasions the true population mean is within the interval, however 5% of the possible random samples you could select will provide a confidence interval which does not include the population mean.
A single number calculated from the data set, that is the best single guess for an unknown parameter. Point estimates can be deduced from a confidence interval by taking the midpoint of the interval.
A range of numbers around the point estimate, within which an unknown parameter is expected to fall. The confidence interval is a widely used type of interval estimate.