What Is a Confidence Interval and How Do You Calculate It?

 

What Is a Confidence Interval?

In statistics, a confidence interval is a range of values likely to contain a population parameter that is unknown. Analysts often use confidence level values of 95% or 99% when calculating confidence intervals. Thus, if a point estimate is generated from a statistically significant population with a mean of 10.00 using a 95% confidence interval of 9.50 to 10.50, it means one is 95% confident that the true value from the population falls within that range.

Statisticians and other analysts use confidence intervals to understand whether their sample estimations, inferences, or predictions match the actual populations. If a confidence interval contains the value of zero (or some other null hypothesis), then one cannot satisfactorily claim that a result from data generated by testing or experimentation is to be attributable to a specific cause rather than chance.

Key Takeaways

• A confidence interval is the probability that a parameter will fall between a pair of values.
• Confidence intervals measure the degree of uncertainty or certainty in a sampling method.
• They are also used in hypothesis testing and regression analysis.
• They are most often constructed using confidence levels of 95% or 99%.

Understanding Confidence Intervals

Confidence intervals measure the degree of uncertainty or certainty in a sampling method. Most commonly, analysts use a 95% or 99% confidence level.

A confidence interval is a range of values, bounded above and below the statistic's mean, that likely would contain an unknown population parameter. Confidencelevel is the probability (percent of certainty) that the confidence interval would contain the true population parameter when you draw a random sample many times.

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