Confidence Intervals

Smithson first introduces the basis of the confidence interval framework and then provides the criteria for best confidence intervals along with the trade-offs between confidence and precision. Next using a reader-friendly style with lots of worked out examples from various disciplines he covers such pertinent topics as: the transformation principle whereby a confidence interval for a parameter may be used to construct an interval for any monotonic transformation of that parameter; confidence intervals on distributions whose shape changes with the value of the parameter being estimated; and the relationship between confidence interval and significance testing frameworks particularly regarding power.