Summary
Contents
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.
Confidence Statements and Interval Estimates
Confidence Statements and Interval Estimates
Let us return to the example confidence statement by the pollster, namely that she is 95% confident that the true percentage vote for a political candidate lies somewhere between 38% and 44%, on the basis of a sample survey from the voting population. Her requirements to make this statement are identical to ...