Phylogenetic Trees Made Easy: A How-To Manual / Edition 4

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Overview

Phylogenetic Trees Made Easy, Fourth Edition helps the reader get started in creating phylogenetic trees from protein or nucleic acid sequence data. Although aimed at molecular and cell biologists, who may not be familiar with phylogenetic or evolutionary theory, it also serves students who have a theoretical understanding of phylogenetics but need guidance in transitioning to a practical application of the methodology. The reader is led, step by step, through identifying and acquiring the sequences to be included in a tree, aligning the sequences, estimating the tree by one of several methods, and drawing the tree for presentation to an intended audience.

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Product Details

  • ISBN-13: 9780878936069
  • Publisher: Sinauer Associates, Incorporated
  • Publication date: 5/9/2011
  • Edition description: New Edition
  • Edition number: 4
  • Pages: 255
  • Product dimensions: 7.05 (w) x 9.25 (h) x 0.59 (d)

Meet the Author

Susan Nolan turned to psychology after suffering a career-ending accident on her second workday as a bicycle messenger. A native of Boston, she graduated from the College of the Holy Cross and earned her Ph.D. in clinical psychology from Northwestern University. Her research involves experimental investigations of the role of gender in the interpersonal consequences of depression and studies of gender and mentoring in science and technology, funded in part by the National Science Foundation. Susan is the Associate Dean of Graduate Studies for the College of Arts and Sciences, as well as an Associate Professor of Psychology, at Seton Hall University in New Jersey. She has served as a statistical consultant to researchers at several universities, medical schools, corporations, and nongovernmental organizations. Recently, she advised Bosnian high school students conducting public opinion research.

Susan's academic schedule allows her to pursue one travel adventure per year, a tradition that she relishes. In recent years she rode her bicycle across the U.S. (despite her earlier crash), swapped apartments to live in Montreal, and explored the Adriatic coast in an intermittently roadworthy 1985 Volkswagen Scirocco. She wrote much of this book while spending a sabbatical year in rural Bosnia-Herzegovina, where her husband, Ivan Bojanic, worked as an advisor to regional governments. Susan and Ivan fell in love with Bosnia – a beautiful country – and bought a small house in the city of Banja Luka as a base for future adventures. They currently reside in New York City, where Susan roots feverishly, if quietly, for the Red Sox.

Tom Heinzen was a 29 year-old college freshman, began graduate school when their fourth daughter was one week old, and is still amazed that he and Donna somehow managed to stay married. A magna cum laude graduate of Rockford College, he earned his Ph.D. in social psychology at the State University of New York at Albany in just three years. He published his first book on frustration and creativity in government two years later, was a research associate in public policy until he was fired over the shape of a graph, consulted for the Johns Hopkins Center for Talented Youth, and then began a teaching career at William Paterson State University of New Jersey. He founded the psychology club, established an undergraduate research conference, and has been awarded various teaching honors while continuing to write journal articles, books, plays, and two novels that support the teaching of general psychology and statistics. He is also the editor of Many Things to Tell You, a volume of poetry by elderly writers.

Tom's wife Donna is a physician assistant who has also volunteered her time in relief work following Hurricane Mitch and Hurricane Katrina. Their daughters are now scattered from Bangladesh to Mississippi to New Jersey and work in public health, teaching, and medicine. He is a mediocre French horn player, an enthusiastic but mediocre tennis player, and an ardent baseball fan (Go Cubs!).

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Table of Contents

1. An Introduction to Statistics and Research Design: The Elements of Statistical Reasoning Two Branches of Statistics: Growing Our Knowledge about Human Behavior
Descriptive Statistics: Organizing, Summarizing, and Communicating Numerical Information Inferential Statistics: Using Samples to Draw Conclusions about a Population Distinguishing Between a Sample and a Population

Variables: Transforming Observations into Numbers

Independent and Dependent Variables: The Main Ingredients of Statistical Thinking
Putting Variables to Work: Independent, Dependent, and Confounding Variables Developing and Assessing Variables: The Reliability and Validity of Tests

An Introduction to Hypothesis Testing: From Hunch to Hypothesis

Types of Research Designs: Experiments, Non-Experiments, and Quasi-Experiments
Experiments and Causality: Control the Confounding Variables Research Designs Other than Experiments: Non-Experiments and Quasi-Experiments One Goal, Two Strategies: Between-subjects Designs vs. Within-subjects Designs

Curiosity, Joy, and the Art of Research Design

Digging Deeper Into the Data: Variations on Standard Research Designs
Outlier Analyses: Does the Exception Prove the Rule?
Archival Studies: When the Data Already Exist

Chapter 2: Descriptive Statistics: Organizing, Summarizing, and Graphical Individual Variables Organizing Our Data: A First Step in Identifying Patterns
Distributions: Four Different Ways to Describe Just One Variable Applying Visual Depictions of Data: Generating Research Questions

Central Tendency: Determining the Typical Score
The Need for Alternative Measures of Central Tendency: Bipolar Disorder Mean: The Arithmetic Average Median: The Middle Score Mode: The Most Common Score The Effect of Outliers on Measures of Central Tendency An Early Lesson in Lying With Statistics: Which Central Tendency is “Best?”

Measures of Variability: Everyone Can’t Be “Typical”
Range: From the Lowest to the Highest Score Variance: The First Step in Calculating Standard Deviation Standard Deviation: Variation from the Mean

Shapes of Distributions: Applying the Tools of Descriptive Statistics
Normal Distributions: The Silent Power Behind Statistics Skewed Distributions: When Our Data Are Not Symmetrical Bimodal and Multimodal Distributions: Identifying Distinctive Populations Kurtosis and Distributions: Tall and Skinny Versus Short and Wide

Digging Deeper into the Data: Alternate Approaches to Descriptive Statistics
The Interquartile Range: An Alternative to the Range Statistics that Don’t Focus on the Mean: Letting the Distribution Guide our Choice of Statistics

Chapter 3: Visual Displays of Data: Graphs That Tell a Story

Uses of Graphs: Clarifying Danger, Exposing Lies, and Gaining Insight
Graphing in the Information Age: A Critical Skill
“The Most Misleading Graph Ever Published”: The Cost and Quality of Higher Education
“The Best Statistical Graph Ever Created”: Napoleon’s Disastrous March to Moscow

Common Types of Graphs: A Graph Designer’s Building Blocks
Scatterplots: Observing Every Data Point Line Graphs: Searching for Trends Bar Graphs: An Efficient Communicator Pictorial Graphs: Choosing Clarity over Cleverness Pie Charts: Are Pie Charts Passé?

How to Build a Graph: Dos and Don’ts
APA Style: Graphing Guidelines for Psychologists Choosing the Type of Graph: Understanding Our Variables The Limitations of Graphic Software: Who is Responsible for the Visual Display?
Creating the Perfect Graph: General Guidelines

Graphing Literacy: Learning to Lie Versus Creating Knowledge
Lying with Statistics and Graphs: Eleven Sophisticated Techniques The Future of Graphs: Breaking the Fourth Wall The Uses and Misuses of Statistics: It’s Not Just What You Draw, It’s How You Draw It

Digging Deeper into the Data: The Box Plot

Chapter 4: Probabilities and Research: The Risks and Rewards of Scientific Sampling

Samples and Their Populations: Why Statisticians Are Stingy!
Decision Making: The Risks and Rewards of Sampling Random Sampling: An Equal Chance of Being Selected Variations on Random Sampling: Cluster Sampling and Stratified Sampling Convenience Sampling: Readily Available Participants Random Assignment: An Equal Chance of Being Assigned to a Condition Variations on Random Assignment: Block Design and Replication

Sampling in the Behavioral Sciences: Why Sampling is Both an Art and a Science
Neither Random Selection, Nor Random Assignment: A Study of Torture Random Assignment, But Not Random Selection: A Study of Expert Testimony Random Selection, But Not Random Assignment: A Study of Children’s Literature

Probability Theory: Distinguishing Between Mere Coincidence and Real Connections
Coincidence and Probability: Why Healthy Skepticism Is Healthy Beyond Confirmation Biases: The Dangers of Groupthink

Probability Theory: The Basics
Expected Relative-Frequency Probability: The Probability of Statistics Independence and Probability: The Gambler’s Fallacy Statistician Sleuths: The Case of Chicago’s Cheating Teachers

Statistics and Probability: The Logic of Inferential Statistics
Dead Grandmothers: Using Probability to Make Decisions Consideration of Future Consequences: Developing Hypotheses Consideration of Future Consequences: Making a Decision about Our Hypotheses

Type I and Type II Errors: Statistical Inferences Can Be Wrong
Type I Errors: Sins of Commission Type II Errors: Sins of Omission

Statistics in Everyday Life: Tying It All Together

The Case of Lush: Testimonial to a Moisturizer Understanding the Meaning of Proof: Statistical Literacy in Consumer Research

Digging Deeper into the Data: The Shocking Prevalence of Type I Errors
Estimating Type I Error in the Medical Literature Medical Findings and Our Own Confirmation Biases

Chapter 5. Correlation: Quantifying the Relation between Two Variables
Correlation: Assessing Associations between Variables
The Need for Standardization: Putting Two Different Variables on the Same Scale

The z Score: Transforming Raw Scores into Standardized Scores

The Pearson Correlation Coefficient: Quantifying a Linear Association
Everyday Correlation Reasoning: Asking Better Questions Calculation of the Pearson Correlation Coefficient: Harnessing the Power of z Scores

Misleading Correlations: Considering the Stories behind the Numbers
Correlation is Not Causation: Invisible Third Variables A Restricted Range: When the Values of One Variable Are Limited The Effect of an Outlier: The Influence of a Single Data Point Reliability and Validity: A Correlation Coefficient Is Only as Good as Our Data

Reliability and Validity: Correlation in Test Construction
Correlation, Psychometrics, and a Super-Heated Job Market: Creating the Measures behind the Research Reliability: Using Correlation to Create a Consistent Test Validity: Using Correlation to Determine Whether We Are Measuring What We Intend to Measure

Digging Deeper into the Data: Partial Correlation

Chapter 6. Regression: Tpols for Predicting Behavior

Regression: Building on Correlation
The Difference between Regression and Correlation: Prediction Versus Relation Linear Regression: Calculating the Equation for a Line using z Scores Only

Reversing the Formula: Transforming z Scores to Raw Scores

Linear Regression: Calculating the Equation for a Line by Converting Raw Scores to z Scores Linear Regression: Calculating the Equation for a Line with Raw Scores

Drawing Conclusions from a Regression Equation: Interpretation and Prediction
Regression: Now Think Again (Realistically)!
What Correlation Can Teach Us about Regression: Correlation Still Isn’t Causation

Regression to the Mean: The Patterns of Extreme Scores The Effect Size for Regression: Proportionate Reduction in Error

Multiple Regression: Predicting from More than One Variable
Multiple Regression: Understanding the Equation Stepwise Multiple Regression and Hierarchical Multiple Regression: A Choice of Tactics

Digging Deeper Into the Data: Structural Equation Modeling (SEM)

Chapter 7. The Power of Standardization: From Description to Inference

The Normal Curve: It’s Everywhere!

Standardization, z Scores, and the Normal Curve: Discovering Reason behind the Randomness
Standardization: Comparing z Scores Putting z Scores to Work: Transforming z Scores to Percentiles

The Central Limit Theorem: How Sampling Creates a Less Variable Distribution
Creating a Distribution of Means: Understanding Why It Works Characteristics of the Distribution of Means: Understanding Why It’s So Powerful

How to Take Advantage of the Central Limit Theorem: Beginning With z Scores
Creating Comparisons: Applying z Scores to a Distribution of Means Estimating Population Parameters from Sample Statistics: Connecting Back

Digging Deeper into the Data: The History of the Normal Curve

Chapter 8. Hypothesis Testing With z Tests: Making Fair Comparisons

The Versatile z Table: Raw Scores, z Scores, and Percentages
From z Scores to Percentages: The Benefits of Standardization From Percentages to z Scores: The Benefits of Sketching the Normal Curve The z Table and Distributions of Means: The Benefits of Unbiased Comparisons

Hypothesis Tests: An Introduction
Assumptions: The Requirements to Conduct Analyses The Six Steps of Hypothesis Testing

Hypothesis Tests: The Single Sample z Test
The z Test: When We Know the Population Mean and the Standard Deviation The z Test: The Six Steps of Hypothesis Testing

The Effect of Sample Size: A Means to Increase the Test Statistic
Increasing Our Test Statistic through Sample Size: A Demonstration The Effect of Increasing Sample Size: What’s Going On

Digging Deeper in the Data: What to Do with Dirty Data

Chapter 9. Hypothesis Testing with t Tests: Making Fair Comparisons between Two Groups

The t Distributions: Distributions of Means When the Parameters Are Not Known
Using a t Distribution: Estimating a Population Standard Deviation from a Sample Calculating a t Statistic for the Mean of a Sample: Using the Standard Error When t and z Are Equal: Very Large Sample Sizes The t Distributions: Distributions of Differences Between Means

Hypothesis Tests: The Single Sample t Test
The Single Sample t Test: When We Know the Population Mean, But Not the Standard Deviation

The t Table: understanding Degrees of Freedom

The t Test: The Six Steps of Hypothesis Testing

Hypothesis Tests: Tests for Two Samples
The Paired Samples t Test: Two Sample Means and a Within-Groups Design The Independent Samples t Test: Two Sample Means and a Between-Groups Design

Digging Deeper into the Data: Exploring Two Group Comparisons
Difference Scores: Are All Differences Created Equal?
Graphing Two Samples: Visualizing Two Sets of Scores

Chapter 10. Hypothesis Testing Using One-Way ANOVA: Comparing Three or More Groups

When to Use the F Distribution: Working With More than Two Samples

A Mnemonic for When to Use a t Distribution or the F Distribution: 't' for Two

The F Distribution: Analyzing Variability to Compare Means Relation of F to t (and z): F as a Squared t for Two Groups and Large Samples

Analysis of Variance (ANOVA): Beyond t Tests
The Problem of Too Many t Tests: Fishing for a Finding The Assumptions for ANOVA: Naming the Ideal Conditions for the Perfect Study

The One-Way Between-Groups ANOVA: Applying the Six Steps of Hypothesis Testing
Everything ANOVA but the Calculations: The Six Steps of Hypothesis Testing The F Statistic: Logic and Calculations Bringing It All Together: What Is the ANOVA Telling Us to Do About the Null Hypothesis?
Why the ANOVA is Not Sufficient: Post-Hoc Tests

Digging Deeper into the Data: Post-Hoc Tests to Determine Which Groups Are Different
Planned and A Priori Comparisons: When Comparisons between Pairs Are Guided by Theory Tukey HSD: An Honest Approach The Bonferroni Test: A More Stringent Post-Hoc Test

Chapter 11. Two-Way ANOVA: Understanding Interactions

Two-Way ANOVA: When the Outcome Depends on More Than One Variable
Why Use a Two-Way ANOVA: The Practicalities and Aesthetics The More Specific Vocabulary of Two-Way ANOVAs: Name That ANOVA Part II Two Main Effects and an Interaction: Three F Statistics and Their Stories

The Layers of ANOVA: Understanding Interactions

Interactions and Public Policy: Using Two-Factor ANOVA to Improve Planning

Interpreting Interactions: Understanding Complexity

Visual Representations of Main Effects and Interactions: Bar Graphs

The Expanded Source Table: Conducting a Two-Way Between Subjects ANOVA
Two-Way ANOVA: The Six Steps of Hypothesis Testing Two-Way ANOVA: Identifying Four Sources of Variability

Interactions: A More Precise Interpretation

Interpreting Interactions: Towards a More Precise Statistical Understanding

Residuals: Separating the Interaction from the Main Effect

Digging Deeper into the Data: More Sophisticated Versions of ANOVA
Within-Groups and Mixed Designs: When the Same Participants Experience More than One Condition

MANOVA, ANCOVA, and MANCOVA: Multiple Dependent Variables and Covariates

Chapter 12. Beyond Hypothesis Testing: Confidence Intervals, Effect Size, and Power

Beyond Hypothesis Testing: Reducing Misinterpretations
Men, Women, and Math: An Accurate Understanding of Gender Differences Beyond Hypothesis Testing: Enhancing Our Samples’ Stories

Confidence Intervals: An Alternative to Hypothesis Testing
Interval Estimation: A Range of Plausible Means
z Distributions: Calculating Confidence Intervals

t Distributions: Calculating Confidence Intervals

Effect Size: Just How Big is the Difference?
Misunderstandings from Hypothesis Testing: When “Significant” Isn’t Very Significant What Effect Size Is: Standardization across Studies Cohen’s d: The Effect Size for a z Test or a t Test
R2: The Effect Size for ANOVA

Statistical Power and Sensitivity: Correctly Rejecting the Null Hypothesis
Calculation of Statistical Power: How Sensitive is a z Test?
Beyond Sample Size: Other Factors that Affect Statistical Power

Digging Deeper into the Data: Meta-Analysis
Meta-Analysis: A Study of Studies The Steps to Conduct a Meta-Analysis The File Drawer Statistic: Where Are All the Null Results?

Chapter 13. Chi Square: Quantifying the Difference between Expectations and Observations

Non-Parametric Statistics: When We’re Not Even Close to Meeting the Assumptions
Non-Parametric Tests: Using the Right Statistical Tool for the Right Statistical Job Non-Parametric Tests: When to Use Them Non-Parametric Tests: Why to Avoid Them Whenever Possible

Chi-square Test for Goodness-of-Fit: When We Have One Nominal Variable
Chi-Square Test for Goodness-of-Fit: The Six Steps of Hypothesis Testing A More Typical Chi-Square Test for Goodness-of-Fit: Evenly Divided Expected Frequencies

Chi-square Test for Independence: When We Have Two Nominal Variables
Chi-Square Test for Independence: The Six Steps of Hypothesis Testing Cramer’s Phi: The Effect Size for Chi square Graphing Chi-square Percentages: Depicting the Relation Visually Relative Risk: How Much Higher Are the Chances of an Outcome?

Digging Deeper into the Data: A Deeper Understanding of Chi square
Standardized Residuals: A Post-Hoc Test for Chi square Chi-Square Controversies: Expectations about Expected Frequencies

Chapter 14. Beyond Chi Square: Commonly Used Non-parametric Tests with Ordinal Data

Non-Parametric Statistics: When the Data Are Ordinal
Hypothesis Tests with Ordinal Data: A Non-parametric Equivalent for Every Parametric Test Examining the Data: Deciding to Use a Non-parametric Test for Ordinal Data

Spearman Rank Order Correlation Coefficient: Quantifying the Association between Two Ordinal Variables
Calculating Spearman’s Correlation: Converting Interval Observations to Rank-Ordered Observations Eye-Balling the Data: Using Your Scientific Common Sense

Non-parametric Hypothesis Tests: Comparing Groups Using Ranks
The Wilcoxon Signed-Rank Test for Matched Pairs: A Non-parametric Test for Within-Subjects Designs The Mann-Whitney U Test: Comparing Two Independent Groups Using Ordinal Data Kruskal-Wallis H Test: Comparing the Mean Ranks of Several Groups

Digging Deeper into the Data: Transforming Skewed Data, the Meaning of Interval Data, and Bootstrapping

Coping with Skew: Data Transformations

Controversies in Non-Parametric Hypothesis Tests: What Really Is an Interval Variable?
Bootstrapping: When the Data Do the Work Themselves

Chapter 15. Choosing a Statistical Test and Reporting the Results: The Process of Statistics.

Before You Even Begin: Choosing the Right Statistical Test
Planning Your Statistics First: How to Avoid That Post-Data Collection Regret Beyond the Statistical Plan: Tips for a Successful Study

Guidelines for Reporting Statistics: The Common Language of Research
Choosing the Right Statistical Test: Questions to Ask Yourself Choosing the Right Statistical Test: Questions to Ask About the Data

Reporting the Statistics: The Results Section of an APA-Style Paper
Telling Your Story: What to Include in a Results Section Defending Your Study: Convincing the Reader that the Results are Worth Reading
“Traditional” Statistics: The Longstanding Way of Reporting Results Statistics Strongly Encouraged by APA: Essential Additions to the “Traditional” Statistics What Not to Include in a Results Section: Keeping the Story Focused Two Excerpts from Results Sections: Understanding the Statistical Story

Unfamiliar Statistics: How to Approach Any Results Section with Confidence

Digging Deeper into the Data: Reporting More Sophisticated Statistical Analyses

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