Intuitive biostatistics

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Title:

Author:

Motulsky, Harvey.

ISBN:

9780195086065

9780195086072

Personal Author:

Motulsky, Harvey.

Publication Information:

New York : Oxford University Press, 1995.

Physical Description:

xx, 386 p. : ill. ; 24 cm.

Contents:

Pt. I. Confidence Intervals -- Pt. II. Comparing Groups with Confidence Intervals -- Pt. III. Introduction to P Values -- Pt. IV. Bayesian Logic -- Pt. V. Correlation and Regression -- Pt. VI. Designing Clinical Studies -- Pt. VII. Common Statistical Tests -- Pt. VIII. Introduction to Advanced Statistical Tests -- Pt. IX. Overviews -- Pt. X. Appendices.

Subject Term:

Medicine -- Research -- Statistical methods.

Copies:

Available:*

Library	Material Type	Item Barcode	Shelf Number	Copy	Status
Searching... IIEMSA	General Books	33168025458569	610.015195 M922.I 1995	1	Searching... Unknown

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Summary

Designed to provide a nonmathematical introduction to biostatistics for medical and health science students, graduate students in the biological sciences, physicians, and researchers, this text explains statistical principles in non-technical language and focuses on explaining the proper scientific interpretation of statistical tests rather than on the mathematical logic of the tests themselves.Intuitive Biostatistics covers all the topics typically found in an introductory statistics text, but with the emphasis on confidence intervals rather than P values, making it easier for students to understand both. Additionally, it introduces a broad range of topics left out of most other introductory texts but used frequently in biomedical publications, including survival curves. multiple comparisons, sensitivity and specificity of lab tests, Bayesian thinking, lod scores, and logistic, proportional hazards and nonlinear regression.By emphasizing interpretation rather than calculation, this text provides a clear and virtually painless introduction to statistical principles for those students who will need to use statistics constantly in their work. In addition, its practical approach enables readers to understand the statistical results published in biological and medical journals.