LEADER 06814nam 22006015 450 001 9910438031103321 005 20200707005358.0 010 $a1-4614-7792-1 024 7 $a10.1007/978-1-4614-7792-1 035 $a(CKB)3710000000002544 035 $a(EBL)1398476 035 $a(SSID)ssj0000962612 035 $a(PQKBManifestationID)11600206 035 $a(PQKBTitleCode)TC0000962612 035 $a(PQKBWorkID)10976075 035 $a(PQKB)11776693 035 $a(DE-He213)978-1-4614-7792-1 035 $a(MiAaPQ)EBC1398476 035 $a(PPN)172419948 035 $a(EXLCZ)993710000000002544 100 $a20130722d2013 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aConverting Data into Evidence$b[electronic resource] $eA Statistics Primer for the Medical Practitioner /$fby Alfred DeMaris, Steven H. Selman 205 $a1st ed. 2013. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2013. 215 $a1 online resource (231 p.) 300 $aDescription based upon print version of record. 311 $a1-4614-7791-3 320 $aIncludes bibliographical references and index. 327 $aPreface; Contents; Chapter 1: Statistics and Causality; What Is Statistics?; What Statistics Is; An Example; Populations and Samples; Probability vs. Nonprobability Samples; Sampling "to" a Population; Statistics and Causal Inference; A Mathematical Definition of "Causal Effect"; How Do We Estimate the ACE?; Example of Latent Self-Selection; Internal vs. External Validity: A Conundrum; Chapter 2: Summarizing Data; Descriptive Statistical Techniques; Quantitative vs. Qualitative Data; Describing Data; Measuring Center and Spread of a Variable's Distribution; The Mean 327 $aPercentiles and the MedianDispersion; Data from the General Social Survey; Describing the Population Distribution; The Normal and t Distributions; Applications: Descriptive Statistics in Action; Tarenflurbil Study; Hydroxychloroquine Study; RALP Study; Brachytherapy Study; Chapter 3: Testing a Hypothesis; The Test of Hypothesis; Let's Roll the Dice; Testing Whether Al's Die Is Loaded; Statement of Hypotheses; Testing the Null Hypothesis; Making a Decision; "Statistically Significant" Results; What About Your Sequence of Die Rolls?; Large-Sample Test of Hypothesis About a Mean 327 $aAssumptions for the TestStatement of Hypotheses; Before Going Further: The Sampling Distribution of a Sample Statistic; Simple Example of a Sampling Distribution; A More Elaborate Example; Sampling Distribution of the Mean for the Large-Sample Test of Hypothesis; The Central Limit Theorem; Test Statistic and P -Value; Summary; Chapter 4: Additional Inferential Procedures; Confidence Intervals and the T Test; Confidence Intervals; Testing the Difference Between Two Means: The T Test; Statement of Hypotheses; Sample Information and the Sampling Distribution; Assumptions for the T Test 327 $aComputation of the Test StatisticFinding the P Value; One-Tailed vs. Two-Tailed Tests; Summary: Hypothesis Testing; Decision Errors and the Power of the Test; Power of the T Test in the Diet Example; T Tests for the GSS Data; Comments About Statistical Tests; P Values, Revisited; Sampling from "The Population"; Application: T Tests and Statistical Power in Action; Gender Difference in Physician Salaries; Power Considerations in Hydroxychloroquine Study; Power in the Arterial Inflammation Study; Chapter 5: Bivariate Statistical Techniques; A Nonparametric Test for the Steak-Diet Example 327 $aComputing the WRSTBivariate Statistics; Bivariate Analysis: Other Scenarios; Qualitative Treatment with More Than Two Levels: ANOVA; Qualitative Treatment and Qualitative Response: ? 2; Calculating the ? 2 Value; Minimum and Maximum Values of ? 2; Measuring the Strength of Association; Quantitative Treatment and Response: The Correlation Coefficient; Testing the Significance of R; The Paired t Test: How Correlation Affects the Standard Error; Summary of Bivariate Statistics; Application: Bivariate Statistics in Action; ANOVA: GGT and Alcohol Consumption 327 $a? 2 : Second-to-Fourth Digit Ratio Study 330 $aConverting Data into Evidence: A Statistics Primer for the Medical Practitioner provides a thorough introduction to the key statistical techniques that medical practitioners encounter throughout their professional careers. These techniques play an important part in evidence-based medicine or EBM. Adherence to EBM requires medical practitioners to keep abreast of the results of medical research as reported in their general and specialty journals. At the heart of this research is the science of statistics. It is through statistical techniques that researchers are able to discern the patterns in the data that tell a clinical story worth reporting. The authors begin by discussing samples and populations, issues involved in causality and causal inference, and ways of describing data. They then proceed through the major inferential techniques of hypothesis testing and estimation, providing examples of univariate and bivariate tests. The coverage then moves to statistical modeling, including linear and logistic regression and survival analysis. In a final chapter, a user-friendly introduction to some newer, cutting-edge, regression techniques will be included, such as fixed-effects regression and growth-curve modeling. A unique feature of the work is the extensive presentation of statistical applications from recent medical literature. Over 30 different articles are explicated herein, taken from such journals. With the aid of this primer, the medical researcher will also find it easier to communicate with the statisticians on his or her research team. The book includes a glossary of statistical terms for easy access. This is an important reference work for the shelves of physicians, nurses, nurse practitioners, physician?s assistants, medical students, and residents. . 606 $aStatistics  606 $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030 606 $aStatistics, general$3https://scigraph.springernature.com/ontologies/product-market-codes/S0000X 615 0$aStatistics . 615 14$aStatistics for Life Sciences, Medicine, Health Sciences. 615 24$aStatistics, general. 676 $a610.727 700 $aDeMaris$b Alfred$4aut$4http://id.loc.gov/vocabulary/relators/aut$0103438 702 $aSelman$b Steven H$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910438031103321 996 $aConverting Data into Evidence$92513589 997 $aUNINA