Confidence Interval: Explained in Simple Words

A confidence interval, in statistics, refers to the probability that a population parameter will fall between a set of values for a certain proportion of times. Analysts often use confidence intervals than contain either 95% or 99% of expected observations. Thus, if a point estimate is generated from a statistical model of 10.00 with a 95% confidence interval of 9.50 - 10.50, it can be inferred that there is a 95% probability that the true value falls within that range.
A confidence interval displays the probability that a parameter will fall between a pair of values around the mean.
Confidence intervals measure the degree of uncertainty or certainty in a sampling method.
They are also used in hypothesis testing and regression analysis.
Statisticians often use p-values in conjunction with confidence intervals to gauge statistical significance.
They are most often constructed using confidence levels of 95% or 99%.
Suppose a group of researchers is studying the heights of high school basketball players. The researchers take a random sample from the population and establish a mean height of 74 inches.
The mean of 74 inches is a point estimate of the population mean. A point estimate by itself is of limited usefulness because it does not reveal the uncertainty associated with the estimate.
Assume the interval is between 72 inches and 76 inches. If the researchers take 100 random samples from the population of high school basketball players as a whole, the mean should fall between 72 and 76 inches in 95 of those samples.

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