Logic and Critical Thinking in the Biomedical Sciences
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ISBN/EAN:
9780128213629
All too often, individuals engaged in the biomedical sciences assume that numeric data must be left to the proper authorities (e.g., statisticians and data analysts) who are trained to apply sophisticated mathematical algorithms to sets of data. This is a terrible mistake. Individuals with keen observational skills, regardless of their mathematical training, are in the best position to draw correct inferences from their own data and to guide the subsequent implementation of robust, mathematical analyses. Volume 2 of Logic and Critical Thinking in the Biomedical Sciences provides readers with a repertoire of deductive non-mathematical methods that will help them draw useful inferences from their own data.Volumes 1 and 2 of Logic and Critical Thinking in the Biomedical Sciences are written for biomedical scientists and college-level students engaged in any of the life sciences, including bioinformatics and related data sciences. - Demonstrates that a great deal can be deduced from quantitative data, without applying any statistical or mathematical analyses - Provides readers with simple techniques for quickly reviewing and finding important relationships hidden within large and complex sets of data - Using examples drawn from the biomedical literature, discusses common pitfalls in data interpretation and how they can be avoided
Jules J. Berman, Ph.D., M.D. holds degrees from MIT, Temple University, and the University of Miami. He served as Chief of Anatomic Pathology, Surgical Pathology, and Cytopathology at the Veterans Administration Medical Center in Baltimore, Maryland, with joint appointments at the University of Maryland Medical Center and at the Johns Hopkins Medical Institutions. He later served at the US National Cancer Institute as a medical officer and as program director for pathology informatics in the Cancer Diagnosis Program. Dr. Berman is a past president of the Association for Pathology Informatics and the 2011 recipient of the association's Lifetime Achievement Award.Jules J. Berman, Ph.D., M.D. holds degrees from MIT, Temple University, and the University of Miami. He served as Chief of Anatomic Pathology, Surgical Pathology, and Cytopathology at the Veterans Administration Medical Center in Baltimore, Maryland, with joint appointments at the University of Maryland Medical Center and at the Johns Hopkins Medical Institutions. He later served at the US National Cancer Institute as a medical officer and as program director for pathology informatics in the Cancer Diagnosis Program. Dr. Berman is a past president of the Association for Pathology Informatics and the 2011 recipient of the association's Lifetime Achievement Award. He has first-authored more than 100 journal articles and has written 18 science books. His most recent titles, published by Elsevier, include: -Taxonomic Guide to Infectious Diseases: Understanding the Biologic Classes of Pathogenic Organisms, 1st edition (2012) -Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information (2013) -Rare Diseases and Orphan Drugs: Keys to Understanding and Treating the Common Diseases (2014) -Repurposing Legacy Data: Innovative Case Studies (2015) -Data Simplification: Taming Information with Open Source Tools (2016) -Precision Medicine and the Reinvention of Human Disease (2018) -Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition (2018) -Taxonomic Guide to Infectious Diseases: Understanding the Biologic Classes of Pathogenic Organisms, 2nd edition (2019)
Jules J. Berman, Ph.D., M.D. holds degrees from MIT, Temple University, and the University of Miami. He served as Chief of Anatomic Pathology, Surgical Pathology, and Cytopathology at the Veterans Administration Medical Center in Baltimore, Maryland, with joint appointments at the University of Maryland Medical Center and at the Johns Hopkins Medical Institutions. He later served at the US National Cancer Institute as a medical officer and as program director for pathology informatics in the Cancer Diagnosis Program. Dr. Berman is a past president of the Association for Pathology Informatics and the 2011 recipient of the association's Lifetime Achievement Award.Jules J. Berman, Ph.D., M.D. holds degrees from MIT, Temple University, and the University of Miami. He served as Chief of Anatomic Pathology, Surgical Pathology, and Cytopathology at the Veterans Administration Medical Center in Baltimore, Maryland, with joint appointments at the University of Maryland Medical Center and at the Johns Hopkins Medical Institutions. He later served at the US National Cancer Institute as a medical officer and as program director for pathology informatics in the Cancer Diagnosis Program. Dr. Berman is a past president of the Association for Pathology Informatics and the 2011 recipient of the association's Lifetime Achievement Award. He has first-authored more than 100 journal articles and has written 18 science books. His most recent titles, published by Elsevier, include: -Taxonomic Guide to Infectious Diseases: Understanding the Biologic Classes of Pathogenic Organisms, 1st edition (2012) -Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information (2013) -Rare Diseases and Orphan Drugs: Keys to Understanding and Treating the Common Diseases (2014) -Repurposing Legacy Data: Innovative Case Studies (2015) -Data Simplification: Taming Information with Open Source Tools (2016) -Precision Medicine and the Reinvention of Human Disease (2018) -Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition (2018) -Taxonomic Guide to Infectious Diseases: Understanding the Biologic Classes of Pathogenic Organisms, 2nd edition (2019)
Autor: | Jules J. Berman |
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EAN: | 9780128213629 |
eBook Format: | ePUB/PDF |
Sprache: | English |
Produktart: | eBook |
Veröffentlichungsdatum: | 08.07.2020 |
Untertitel: | Volume 2: Deductions Based Upon Quantitative Data |
Kategorie: | |
Schlagworte: | Approximation Bayesian analysis Biological diversification Blockchain Case report Causal paradox Causation Counting Data range Disease incidence Dynamic range Equations Estimation Gaussian distribution Genome size Homeostasis List of |
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