Statistical Methods and Reasoning for the Clinical Sciences
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Additional Book Details
Statistical Methods and Reasoning for the Clinical Sciences: Evidence-Based Practice provides practitioners with the scientific literacy needed to understand statistical methods in order to increase the accuracy of their diagnoses.
With case studies included on a companion website, this text will help readers comprehend how the process of clinical research relates to the scientific method of problem solving. Readers will understand the importance of three key, interrelated tasks involved in a research study: description (why it was done), explanation (what was done and to whom), and contextualization (how the results relate to other bodies of knowledge).
This text also examines the following:
The two basic elements of statistical reasoning that constitute evidence-based practice: deductive inference (from effect to cause) and inductive inference (from cause to effect).
Classical statistical methods-statistical terms/vocabulary, population parameters, and sampling methods-as well as descriptive statistical methods-measures, correlation, and regression.
The fundamentals of statistical inference that include testing hypotheses using a z-test, t-test, ANOVA, and MANOVA.
The concept of probability, through various concrete examples and a step-by-step approach, which is a fundamental part of the clinical decision-making process.
Evidence-based probabilistic methods called Minimum Bayes Factor (MBF) for measuring the strength of clinical evidence more precisely and as an alternative to classical testing hypotheses methods.
Rationales and procedures of other statistical methods frequently seen in clinical literature, like meta-analysis, nonparametric methods, categorical analyses, and single subject designs.