Fundamental statistical principles for the neurobiologist : a survival guide / Stephen W. Scheff, University of Kentucky Sanders-Brown Center on Aging, Lexington, KY, USA

Published

Amsterdam ; Boston : Academic Press is an imprint of Elsevier, [2016]

Title page; Table of Contents; Copyright; Dedication; Preface; About the Author; Quote; 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; Summary; 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 TestsBias; Summary; Chapter 3. Statistic Essentials; Types of Data; Nominal Data; Ordinal Data; Interval Data; Ratio Data; Discrete and Continuous Data; Measures of Central Tendency; Variance; 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?; Summary; Chapter 4. Graphing Data; How to Graph Data; Box and Whisker Plots; Scatter Plots; Alternative Graphing Procedures; Indicating Significance on a Graph; Summary

Chapter 5. Correlation and RegressionCorrelation; Pearson's Product-Moment Correlation Coefficient; Spearman's Rank Coefficient and Kendall's Tau; Regression (Least Squares Method); Summary; 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 VarianceSphericity; Summary; 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; Summary; 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; Chi-Square; Fisher's Exact Test

Kruskal-Wallis One-Way Analysis of VarianceFriedman 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; Summary; Chapter 9. Outliers and Missing Data; Reasons for Outliers; Removing Outliers; Missing Data; Summary; Chapter 10. Statistic Extras; Statistics Speak; How to Read Statistical Equations; Important Statistical Symbols; Index