Question

Give full explanation about confidence interval with examples​

Answer 1

Answer:

Statisticians use confidence intervals to measure uncertainty. For example, a researcher selects different samples randomly from the same population and computes a confidence interval for each sample. The resulting datasets are all different; some intervals include the true population parameter and others do not.

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Answer 2

Answer:

A confidence interval, in statistics, refers to the probability that a population parameter will fall between two set values for a certain proportion of times. Confidence intervals measure the degree of uncertainty or certainty in a sampling method. A confidence interval can take any number of probabilities, with the most common being a 95% or 99% confidence level.

Explanation:

Statisticians use confidence intervals to measure uncertainty. For example, a researcher selects different samples randomly from the same population and computes a confidence interval for each sample. The resulting datasets are all different; some intervals include the true population parameter and others do not.

A Confidence interval is a range of values that likely would contain an unknown population parameter. Confidence level refers to the percentage of probability, or certainty, that the confidence interval would contain the true population parameter when you draw a random sample many times. Or, in the vernacular, "We are 99% certain (confidence level) that most of these datasets (confidence intervals) contain the true population parameter."

Important:

Confidence interval and confidence level are interrelated but are not exactly the same.