The Biostatistics Cookbook

The Biostatistics Cookbook : The Most User-Friendly Guide for the Bio/Medical Scientist

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We live in a very uncertain world. Variation surrounds our work. There is noise in our experiments, in our measurements, and in our test subjects. From all these sources of uncertainty and variation, we try to extract a coherent picture of very complex and sometimes dynamic, biological and chemical processes. In fact, one of our major challenges is to separate this signal, the 'real' biology or chemistry, from the noise. The tools developed to do this are called, collectively, biostatistics. Any tool, even a hammer, can be misused. This could result, at best, in inefficiency, and, at worst, in disaster. With the advent of newer, us- friendly statistical software packages, desk top computing, and point-a- click technologies, it is easier than ever to make mistakes in your analyses. The beauty of having access to so much computing power is that you can now enjoy ultimate flexibility in data processing: that can also be a problem. Ask your computer to produce a particular analysis, report or graphic, and that is exactly what you will get: if you happen to have asked for the wrong thing it will be produced just as quickly, and you will probably never know it was wrong. One aim of this handbook is to help you choose the correct tool for the job at hand, understand its strengths and weaknesses, and to help you recognize when you should seek expert advice. We describe biostatistics as a collection of tools for very good reasons.
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Product details

  • Hardback | 172 pages
  • 163.1 x 246.4 x 15mm | 403.7g
  • Dordrecht, Netherlands
  • English
  • 1996 ed.
  • 36 Tables, black and white; IV, 172 p.
  • 0792338847
  • 9780792338840

Table of contents

Introduction. 1: Description. Populations, Distributions, and Samples. Measures of Central Tendency. Data Dispersion, Noise, and Error. Graphics. 2: Inference. Comparing a Sample Mean to a Population with Known Mean and Variance - The One Sample z-Test. Comparing a Sample Mean to a Population with Known Mean and Unknown Variance - The One Sample t-Test. Comparing Before and After Data - The Two Sample Paired t-Test. Comparing Two Means - The Two Sample Unpaired t-Test. Comparing Three or More Means - The One Way Analysis of Variance. Comparing Two or More Proportions: Proportions Tests and Chi-Square (chi2). Distribution-Free Measures: Non-Parametric Tests. 3: Estimation. Data Relationships: Association and Correlation. Data Relationships: Mathematical Models and Linear Regression. Complex Data Relationships: Mathematical Models and Non-Linear Regression. 4: Design of a Statistical Experiment. Index.
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