# Confidence analysis

In statistics , a confidence interval CI is a type of estimate computed from the statistics of the observed data. This proposes a range of plausible values for an unknown parameter. The interval has an associated confidence level that the true parameter is in the proposed range. This is more clearly stated as: the confidence level represents the probability that the unknown parameter lies in the stated interval.

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

The results of any statistical analysis should include the confidence intervals for estimated parameters. How confident are you that you can explain what they mean? Even those of us who have a solid understand of confidence intervals can get tripped up by the wording.

As noted in earlier modules a key goal in applied biostatistics is to make inferences about unknown population parameters based on sample statistics. There are two broad areas of statistical inference, estimation and hypothesis testing. Estimation is the process of determining a likely value for a population parameter e. In practice, we select a sample from the target population and use sample statistics e. The sample should be representative of the population, with participants selected at random from the population.