Smithson first introduces the basis of the confidence interval
framework and then provides the criteria for "best" confidence
intervals, along with the tradeoffs 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.
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