Sampling Probability and Inference
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چکیده
The second part of the book looks into the probabilistic foundation of statistical analysis, which originates in probabilistic sampling, and introduces the reader to the arena of hypothesis testing. Chapter 5 explores the main random and controllable source of error, sampling, as opposed to non-sampling errors, potentially very dangerous and unknown. It shows the statistical advantages of extracting samples using probabilistic rules, illustrating the main sampling techniques with an introduction to the concept of estimation associated with precision and accuracy. Non-probability techniques, which do not allow quantification of the sampling error, are also briefly reviewed. Chapter 6 explains the principles of hypothesis testing based on probability theories and the sampling principles of previous chapter. It also explains how to compute confidence intervals and how statistics allow one to test hypotheses on one or two samples. Chapter 7 extends the discussion to the case of more than two samples, through a class of techniques which goes under the name of analysis of variance. The principles are explained and with the aid of SPSS examples the chapter provides a quick introduction to advanced and complex designs under the broader general linear modelling approach.
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