CandleFocus Glossary Editor
Confidence Intervals are used to estimate the true value of a population parameter based on a sample statistic. A confidence interval is the range of values around a statistic that is likely to contain the true value of the population parameter, given the level of certainty one chooses for the interval. Confidence intervals are an essential tool for statisticians because they provide a measure of uncertainty that can be applied to any statement about the population.
A confidence interval is used to express the degree of uncertainty surrounding an estimate of population parameter. It is calculated from a randomly sampled population. It is expressed as a percentage (usually 95% or 99%) and it tells us the probability that the true population parameter is between the upper and lower values of the interval (the confidence limits). If the 95% confidence interval contains the true population parameter, there is a 95% chance that the population parameter is within the interval.
The main benefit of using confidence intervals is to quantify the uncertainty associated with a parameter estimate. This uncertainty can help one to decide whether to accept or reject a hypothesis, evaluate a regression model, or come to other decisions making. In addition, it can also be used to create test statistics for hypothesis testing.
When constructing a confidence interval, one must determine the confidence level desired. Commonly used confidence levels are 95% and 99%, but other levels such as 80% and 90% are sometimes used as well. The selection of the confidence level will depend on the specific application and how much uncertainty one is comfortable with. As the confidence level increases, the certainty of the interval also increases, but the interval becomes wider.
In summary, confidence intervals are an essential tool used by statisticians in order to estimate a population parameter, quantify the uncertainty associated with parameter estimates and make statistical decisions. They are constructed with a chosen level of confidence and provide an estimated range in which the true value of a statistic falls.
A confidence interval is used to express the degree of uncertainty surrounding an estimate of population parameter. It is calculated from a randomly sampled population. It is expressed as a percentage (usually 95% or 99%) and it tells us the probability that the true population parameter is between the upper and lower values of the interval (the confidence limits). If the 95% confidence interval contains the true population parameter, there is a 95% chance that the population parameter is within the interval.
The main benefit of using confidence intervals is to quantify the uncertainty associated with a parameter estimate. This uncertainty can help one to decide whether to accept or reject a hypothesis, evaluate a regression model, or come to other decisions making. In addition, it can also be used to create test statistics for hypothesis testing.
When constructing a confidence interval, one must determine the confidence level desired. Commonly used confidence levels are 95% and 99%, but other levels such as 80% and 90% are sometimes used as well. The selection of the confidence level will depend on the specific application and how much uncertainty one is comfortable with. As the confidence level increases, the certainty of the interval also increases, but the interval becomes wider.
In summary, confidence intervals are an essential tool used by statisticians in order to estimate a population parameter, quantify the uncertainty associated with parameter estimates and make statistical decisions. They are constructed with a chosen level of confidence and provide an estimated range in which the true value of a statistic falls.