Basic principles of statistical inference.
In this chapter, we discuss the fundamental principles behind two of the most frequently used statistical inference procedures: confidence interval estimation and hypothesis testing, both procedures are constructed on the sampling distributions that we have learned in previous chapters. To better understand these inference procedures, we focus on the logic of statistical decision making and the role that experimental data play in the decision process. Numerical examples are used to illustrate the implementation of the discussed procedures. This chapter also introduces some of the most important concepts associated with confidence interval estimation and hypothesis testing, including P values, significance level, power, sample size, and two types of errors. We conclude the chapter with a brief discussion on statistical and practical significance of test results.
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Wanzhu
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Division of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA.
D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006333:Heart Failure / Q000097:blood / Q000473:pathology; D006801:Humans; D008297:Male; D020097:Natriuretic Peptide, Brain / Q000097:blood; D017430:Prostate-Specific Antigen / Q000097:blood; D011471:Prostatic Neoplasms / Q000097:blood / Q000601:surgery
D020097:Natriuretic Peptide, Brain; D017430:Prostate-Specific Antigen