Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences 1st Edition by Paul Mitchell ISBN 1119437636 9781119437635

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Product details:
ISBN 10: 1119437636
ISBN 13: 9781119437635
Author: Paul Mitchell

Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences provides clear instructions on applying statistical analysis techniques to pharmacological data. Written by an experimental pharmacologist with decades of experience teaching statistics and designing preclinical experiments, this reader-friendly volume explains the variety of statistical tests that researchers require to analyze data and draw correct conclusions.

Detailed, yet accessible, chapters explain how to determine the appropriate statistical tool for a particular type of data, run the statistical test, and analyze and interpret the results. By first introducing basic principles of experimental design and statistical analysis, the author then guides readers through descriptive and inferential statistics, analysis of variance, correlation and regression analysis, general linear modelling, and more. Lastly, throughout the textbook are numerous examples from molecular, cellular, in vitro, and in vivo pharmacology which highlight the importance of rigorous statistical analysis in real-world pharmacological and biomedical research.

Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences 1st Edition Table of contents:

Part 1: Introduction to Experimental Design and Statistics

1. The Role of Experimental Design in Biomedical Research
2. Basics of Statistical Analysis: A Primer
3. Ethical Considerations in Biomedical Experimentation

Part 2: Designing Experiments in Pharmacology
4. Key Principles of Experimental Design
5. Types of Experimental Studies: In Vitro, In Vivo, and Clinical Trials
6. Randomization and Blinding: Ensuring Validity and Reducing Bias
7. Sample Size Determination and Power Analysis

Part 3: Data Collection and Management
8. Best Practices for Data Collection in Biomedical Research
9. Handling Missing Data and Outliers
10. Data Visualization: Creating Effective Graphs and Charts

Part 4: Statistical Methods for Biomedical Sciences
11. Descriptive Statistics: Summarizing Data
12. Hypothesis Testing: Concepts and Applications
13. Parametric vs. Non-Parametric Tests: Choosing the Right Test
14. Comparing Groups: t-tests, ANOVA, and Post-Hoc Analyses
15. Correlation and Regression Analysis

Part 5: Advanced Statistical Techniques
16. Multivariate Analysis: Principles and Applications
17. Survival Analysis in Biomedical Research
18. Bayesian Statistics in Pharmacological Studies
19. Meta-Analysis: Synthesizing Research Findings

Part 6: Applications in Pharmacology
20. Dose-Response Studies: Models and Analysis
21. Pharmacokinetics and Pharmacodynamics: Statistical Approaches
22. Biomarker Validation and Analysis

Part 7: Statistical Software and Tools
23. Introduction to Statistical Software: SPSS, R, and Python
24. Performing Statistical Tests Using Software
25. Automating Data Analysis and Reporting

Part 8: Common Pitfalls and Best Practices
26. Avoiding Common Errors in Experimental Design
27. Interpreting Statistical Results: From P-Values to Confidence Intervals
28. Reporting and Publishing Biomedical Research

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