Estimation of population parameters is an important aspect of Inferential Statistics. Estimates of parameters like mean and proportion which are derived from data collected from samples are often used in statements describing the true parameters of population.
The parameter estimates are of two types, Point and interval estimates. The reliability of a Point estimate as a good estimate is often difficult to find and hence its accuracy is generally questionable, Hence the interval estimates are generally preferred by statisticians over Point estimates.An interval estimate is a range of values to estimate the parameter in question, The interval may or may not contain the parameter. Confidence Interval is an Interval estimate for the parameter which also gives the probability of the true parameter falling in that interval.

The probability that an interval estimate will contain the parameter is expressed as the confidence level.

A confidence interval is a specific interval estimate of a parameter determined by sample data and a specific confidence level of the estimate.

The central limit theorem for sampling distributions is used in determining the confidence interval of a parameter like mean or proportion.

The various formulas used to calculate the confidence interval are based on the sample size and the confidence level used.

Confidence level is a degree of confidence expressed as percents like 90%, 95% and 99% prior assigned to the data before the confidence interval is calculated. There is a general trade off between the confidence level and the range of the confidence interval. When greater confidence levels are assigned the confidence interval becomes wider.