Introduction to Statistics for Biology

Introduction to Statistics for Biology

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Even though an understanding of experimental design and statistics is central to modern biology, undergraduate and graduate students studying biological subjects often lack confidence in their numerical abilities. Allaying the anxieties of students, Introduction to Statistics for Biology, Third Edition provides a painless introduction to the subject while demonstrating the importance of statistics in contemporary biological studies. New to the Third Edition * More detailed explanation of the ideas of elementary probability to simplify the rationale behind hypothesis testing, before moving on to simple tests * An emphasis on experimental design and data simulation prior to performing an experiment * A general template for carrying out statistical tests from hypothesis to interpretation * Worked examples and updated Minitab analyses and graphics * A CD-ROM that contains a free trial version of Minitab Using Minitab throughout to present practical examples, the authors emphasize the interpretation of computer output. With its nontechnical approach and practical advice, this student-friendly introductory text lays the foundation for the advanced study of statistical analysis.show more

Product details

  • Hardback | 296 pages
  • 152.4 x 233.68 x 17.78mm | 362.87g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • Revised
  • 3rd Revised edition
  • 78 black & white illustrations
  • 1584886528
  • 9781584886525
  • 1,402,370

Table of contents

PREFACE HOW LONG IS A WORM? Introduction Sampling a Population The Normal Distribution Probability Continuous Measurements-Worms Again Expressing Variability CONFIDENCE INTERVALS The Importance of Confidence Intervals Calculating Confidence Intervals Another Way of Looking At It Your First Statistical Test One- and Two-Tailed Tests The Other Side of the Coin-Type II Errors Recap-Hypothesis Testing A Complication Testing Fish with t Minitab Does a One-Sample t-Test 95% CI for Worms Anatomy of Test Statistics COMPARING THINGS: TWO SAMPLE TESTS A Simple Case Matched-Pairs t-Test Another Example-Testing Twin Sheep Independent Samples: Comparing Two Populations Calculation of Independent Samples t-Test One- and Two-Tailed Tests-A Reminder Minitab Carries Out a Two-Sample t-Test Pooling the Variances? PLANNING AN EXPERIMENT Principles of Sampling Principles of Experimental Design Recording Data and Simulating an Experiment Simulating Your Experiment PARTITIONING VARIATION AND CONSTRUCTING A MODEL It's Simple ... But Not That Simple The Example: Field Margins in Conservation The Idea of a Statistical Model Laying Out the Experiment Sources of Variation: Random Variation The Model ANALYZING YOUR RESULTS: IS THERE ANYTHING THERE? Is Spider Abundance Affected by Treatment? Why Not Use Multiple t-Tests? ANOVA for a Wholly Randomized Design Comparing the Sources of Variation The Two Extremes of Explanation: All or Nothing The ANOVA Table Testing Our Hypothesis Including Blocks: Randomized Complete Block Designs Analyzing the Spider Data Set in Minitab The Assumptions behind ANOVA and How to Test Them Another Use for the F-Test: Testing Homogeneity of Variance INTERPRETING YOUR ANALYSIS: FROM HYPOTHESIS TESTING TO BIOLOGICAL MEANING Treatment Means and Confidence Intervals Difference between Two Treatment Means Getting More Out of an Experiment: Factorial Designs and Interactions Getting More Out of the Analysis: Using the Factorial Design to Ask More Relevant Questions Interactions Adding Blocking to the Factorial Analysis How to Interpret Interaction Plots: The Plant Hormone Experiment Loss of Data and Unbalanced Experiments Limitations of ANOVA and the General Linear Model (GLM) RELATING ONE VARIABLE TO ANOTHER Correlation Calculating the Correlation Coefficient, and a New Idea: Covariance Regression Linear Regression The Model Interpreting Hypothesis Tests in Regression A Further Example of Linear Regression Assumptions The Importance of Plotting Observations Confidence Intervals Standard Error of Prediction (Prediction Interval) Caution in the Interpretation of Regression and Correlation CATEGORICAL DATA The Chi-Squared Goodness-of-Fit Test A More Interesting Example: Testing Genetic Models Contingency Analysis: Chi-Squared Test of Proportions A Further Example of a Chi-Squared Contingency Test Beyond Two-Dimensional Tables: The Likelihood Ratio Chi-Square NONPARAMETRIC TESTS Introduction Basic Ideas A Taxonomy of Tests Single-Sample Tests Matched-Pairs Tests Independent Samples Two Quantitative Variables: Spearman's Rank Correlation Why Bother with Parametric Tests? MANAGING YOUR PROJECT Choosing a Topic and a Supervisor Common Mistakes General Principles of Experimental Design and Execution Analyzing Your Data and Writing the Report Structure The First Draft Illustrating Results What It Is All About: Getting Through Your Project APPENDIX A: AN INTRODUCTION TO MINITAB Conventions Used in This Book Starting Up Help Data Entry Looking at the Worms Data Updating Graphs Stacking and Unstacking-A Useful Trick Looking Up Probabilities Report Writer The Minitab Command Line Saving Your Session APPENDIX B: STATISTICAL POWER AND SAMPLE SIZE APPENDIX C: STATISTICAL TABLES APPENDIX D: REFERENCES AND FURTHER READING APPENDIX E: STATISTICAL TESTS INDEXshow more

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