Short description/annotation
Student-friendly introduction to statistics, essential for bioscience undergraduates, particularly those without a strong mathematical background.
Main description
Statistics Explained is a reader-friendly introduction to experimental design and statistics for undergraduate students in the life sciences, particularly those who do not have a strong mathematical background. Hypothesis testing and experimental design are discussed first. Statistical tests are then explained using pictorial examples and a minimum of formulae. This class-tested approach, along with a well-structured set of diagnostic tables will give students the confidence to choose an appropriate test with which to analyse their own data sets. Presented in a lively and straight-forward manner, Statistics Explained will give readers the depth and background necessary to proceed to more advanced texts and applications. It will therefore be essential reading for all bioscience undergraduates, and will serve as a useful refresher course for more advanced students.
Table of contents:
Preface; 1. Introduction; 2. 'Doing Science' - hypotheses, experiments and disproof; 3. Collecting and displaying data; 4. Introductory concepts of experimental design; 5. Probability helps you make a decision about your results; 6. Working from samples - data, populations and statistics; 7. Normal distributions - test for comparing the means of one or two samples; 8. Type 1 and Type 2 error, power and sample size; 9. Single factor analysis of variance; 10. Multiple comparisons after ANOVA; 11. Two factor analysis of variance; 12. Important assumptions of analysis of variance: transformations and a test for equality of variances; 13. Two factor analysis of variance without replication, and nested analysis of variance; 14. Relationships between variables: linear correlation and linear regression; 15. Simple linear regression; 16. Non-parametric statistics; 17. Non-parametric tests for nominal scale data; 18. Non-parametric tests for ratio, interval or ordinal scale data; 19. Choosing a test; 20. Doing science responsibly and ethically.
Student-friendly introduction to statistics, essential for bioscience undergraduates, particularly those without a strong mathematical background.
Main description
Statistics Explained is a reader-friendly introduction to experimental design and statistics for undergraduate students in the life sciences, particularly those who do not have a strong mathematical background. Hypothesis testing and experimental design are discussed first. Statistical tests are then explained using pictorial examples and a minimum of formulae. This class-tested approach, along with a well-structured set of diagnostic tables will give students the confidence to choose an appropriate test with which to analyse their own data sets. Presented in a lively and straight-forward manner, Statistics Explained will give readers the depth and background necessary to proceed to more advanced texts and applications. It will therefore be essential reading for all bioscience undergraduates, and will serve as a useful refresher course for more advanced students.
Table of contents:
Preface; 1. Introduction; 2. 'Doing Science' - hypotheses, experiments and disproof; 3. Collecting and displaying data; 4. Introductory concepts of experimental design; 5. Probability helps you make a decision about your results; 6. Working from samples - data, populations and statistics; 7. Normal distributions - test for comparing the means of one or two samples; 8. Type 1 and Type 2 error, power and sample size; 9. Single factor analysis of variance; 10. Multiple comparisons after ANOVA; 11. Two factor analysis of variance; 12. Important assumptions of analysis of variance: transformations and a test for equality of variances; 13. Two factor analysis of variance without replication, and nested analysis of variance; 14. Relationships between variables: linear correlation and linear regression; 15. Simple linear regression; 16. Non-parametric statistics; 17. Non-parametric tests for nominal scale data; 18. Non-parametric tests for ratio, interval or ordinal scale data; 19. Choosing a test; 20. Doing science responsibly and ethically.