Using lots of easy to understand examples from different disciplines, the author introduces the basis of the confidence interval framework and provides the criteria for `best' confidence intervals, along with the trade-offs between confidence and precision. The book 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.