What is the meaning of confidence interval with respect to evaluation of analytical data?
The confidence interval is an interval estimate for a parameter value. It is construced in a way so that, in the long run, a given proportion of these intervals will include the unknown true parameter value. The proportion is given by the "level of confidence". For instance, you can expect that at least 90% of (a large series of) 90% confidence intervals will include the unknown true values of the parameters.
Value depends on the parameter and the error model. Statistic software calculate such intervals, so a user actually doesn't need to know the technical details. A frequent problem is to give the CI for a mean value (xbar). This is calulated as xbar plusminus standarderror * t-quantile. The t-quantile is taken to get the desired confidence level.
Value gives you an impression of the precision of the parameter estimate. Values spanned by this interval are seen as "not too unexpected to be true". CI's are actually a frequentist tool, but a further interpretation is Bayesian: given a flat prior, the CI is identical to the maximum a posteriori interval ("credible interval"). Here, the interpretation is inverse. Instead of saying that at least a given proportion of such intervals will include the true value,
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