A 99 percent confidence interval approach is more likely than a 95 percent confidence interval procedure to provide intervals that contain the population parameter. A confidence range of 20% to 40% indicates that the population proportion is between 20% and 40%. A confidence interval can be constructed using the percentile method or the normal approximation method. There are many ways to construct a confidence interval, but they all involve calculating two numbers: (1) a point estimate of the population parameter and (2) a measure of uncertainty called a standard error. The goal when constructing a confidence interval is to include the true value of the parameter with enough certainty so that we may report that we do indeed have an accurate estimate of it. In order to do this, we need to make sure that our confidence interval contains the true value of the parameter almost every time. We can only do this by making sure that the probability of the confidence interval not containing the true value is very small. This is where the concept of confidence intervals comes in. A confidence interval provides information about how certain we are about the parameter value. If the standard error is small, then we can say with high confidence that the true population parameter lies within the interval. In statistics, a confidence interval is a range of values for which we can be confident that the true value of the parameter lies within that range. There are two types of confidence intervals: one-sided and two-sided.