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Fundamental Statistical Principles for Neurobiologists introduces readers to basic experimental design and statistical thinking in a comprehensive, relevant manner. This book is an introductory statistics book that covers fundamental principles written by a neuroscientist who understands the plight of the neuroscience graduate student and the senior investigator. It summarizes the fundamental concepts associated with statistical analysis that are useful for the neuroscientist, and provides understanding of a particular test in language that is more understandable to this specific audience, with the overall purpose of explaining which statistical technique should be used in which situation. Different types of data are discussed such as how to formulate a research hypothesis, the primary types of statistical errors and statistical power, followed by how to actually graph data and what kinds of mistakes to avoid. Chapters discuss variance, standard deviation, standard error, mean, confidence intervals, correlation, regression, parametric vs. nonparametric statistical tests, ANOVA, and post hoc analyses. Finally, there is a discussion on how to deal with data points that appear to be "outliers" and what to do when there is missing data, an issue that has not sufficiently been covered in literature.
- An introductory guide to statistics aimed specifically at the neuroscience audience
- Contains numerous examples with actual data that is used in the analysis
- Gives the investigators a starting pointing for evaluating data in easy-to-understand language
- Explains in detail many different statistical tests commonly used by neuroscientists
Neuroscientists, graduate students/post-docs in biological and biomedical sciences
- About the Author
- Chapter 1. Elements of Experimentation
- Reason for Investigation
- What to Test
- Levels and Outcome Measures
- Site Preparation and Controls
- Troublesome Variables
- What Do You Do First When You Want to Run an Experiment
- Types of Experimental Design
- Chapter 2. Experimental Design and Hypothesis
- Hypothesis—Asking the Right Research Question
- Null Hypothesis (HO) and Alternative Hypothesis (HA)
- What is Probability Anyway?
- Statistical Significance
- What is a Significant Experiment?
- One-Tailed versus Two-Tailed Tests
- Chapter 3. Statistic Essentials
- Types of Data
- Nominal Data
- Ordinal Data
- Interval Data
- Ratio Data
- Discrete and Continuous Data
- Measures of Central Tendency
- Standard Deviation
- Standard Error of the Mean
- Confidence Interval
- Statistical Myth Concerning Confidence Intervals
- What is Meant by “Effect Size”?
- What is a Z Score?
- Degrees of Freedom
- Why n–1?
- Chapter 4. Graphing Data
- How to Graph Data
- Box and Whisker Plots
- Scatter Plots
- Alternative Graphing Procedures
- Indicating Significance on a Graph
- Chapter 5. Correlation and Regression
- Pearson's Product–Moment Correlation Coefficient
- Spearman's Rank Coefficient and Kendall's Tau
- Regression (Least Squares Method)
- Chapter 6. One-Way Analysis of Variance
- Analysis of Variance
- Student's t-Test
- Comparing Three or More Independent Groups
- Completely Randomized One-Way ANOVA
- Partitioned Variance
- Reporting ANOVA Results
- Homogeneity of Variance
- Multiple Comparisons
- Multiple t-Tests
- False Discovery Rate
- Common Post Hoc Tests
- How to Choose Which MCP (Post Hoc) to Employ after an ANOVA
- One-Way Repeated Measures (Within-Subject) Analysis of Variance
- Chapter 7. Two-Way Analysis of Variance
- Concept of Interaction
- Difference between One-Way and Two-Way Analysis of Variance
- Interpreting a Two-Way Analysis of Variance (What Do These Results Actually Tell Us?)
- Two-Way Repeated Measure Analysis of Variance
- Chapter 8. Nonparametric Statistics
- Sign Test
- Wilcoxon Matched Pairs Signed Rank Test (Wilcoxon Signed Rank Test)
- Median Test
- Wilcoxon Rank Sum Test (Mann–Whitney U Test)
- Kolmogorov–Smirnov Two-Sample Test
- Fisher's Exact Test
- Kruskal–Wallis One-Way Analysis of Variance
- Friedman One-Way Repeated Measure Analysis of Variance by Ranks
- Spearman's Rank Order Correlation
- Kendall Rank Order Correlation Coefficient
- Nonparametric and Distribution-Free Are Not Really the Same
- Chapter 9. Outliers and Missing Data
- Reasons for Outliers
- Removing Outliers
- Missing Data
- Chapter 10. Statistic Extras
- Statistics Speak
- How to Read Statistical Equations
- Important Statistical Symbols
- No. of pages:
- © Academic Press 2016
- 11th February 2016
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Stephen W. Scheff, Ph.D. is currently the Associate Director of the Sanders-Brown Center on Aging and a Professor in the Department of Anatomy & Neurobiology at the University of Kentucky. He graduated from Washington University in St. Louis with a degree in psychology and attained both a MA and Ph.D. in physiological psychology from the University of Missouri in Columbia, MO. He spent 6 years as a postdoctoral fellow/ staff scientist at the University of California – Irvine in the Department of Psychobiology. The author has been a member of the Society for Neuroscience since 1974 and a member of the Neurotrauma Society for over 10 years. He has served on numerous NIH study sections and DOD review panels. Dr. Scheff has worked in the fields of neuroplasticity, neurotrauma, and neurodegenerative diseases for the past 45 years and has published using a wide variety of techniques including behavior, neurophysiology, neuroanatomy, cell and molecular signaling and neurochemistry. He has taught human brain anatomy in the College of Medicine for more than 35 years and has trained numerous graduate students and postdoctoral fellows in the art of experimental design and statistics.
Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
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