# Statistics for People Who (Think They) Hate Statistics

- Neil J. Salkind - The University of Kansas, USA

**Sixth Edition**of Neil J. Salkind’s best-selling

**Statistics for People Who (Think They) Hate Statistics**eases student anxiety around an often intimidating subject with a humorous, personable, and informative approach. Salkind guides students through various statistical procedures, beginning with descriptive statistics, correlation, and graphical representation of data, and ending with inferential techniques and analysis of variance. New to this edition is an introduction to working with large data sets and videos of the author demonstrating the various statistical techniques, available via the accompanying free interactive eBook.

**Interactive eBook also available—FREE when bundled with the print version!**

**See the Power and Value of the Interactive eBook**

Your students save when you bundle the print version with the interactive eBook version. Order using bundle ISBN 978-1-5063-6445-2. Learn more.

**CORRECTIONS:**A small list of known errors in the first printing of the 6th edition is posted at the author’s website: statisticsforpeople.com/Corrections.html

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What You Will Learn in This Chapter |

Why Statistics? |

And Why SPSS? |

A 5-Minute History of Statistics |

Statistics: What It Is (and Isn’t) |

What Are Descriptive Statistics? |

What Are Inferential Statistics? |

In Other Words . . . |

What Am I Doing in a Statistics Class? |

Ten Ways to Use This Book (and Learn Statistics at the Same Time!) |

About Those Icons |

Key to Difficulty Icons |

Glossary |

Real-World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

Computing the Mean |

Computing a Weighted Mean |

Computing the Median |

Computing the Mode |

Apple Pie à la Bimodal |

When to Use What Measure of Central Tendency (and All You Need to Know About Scales of Measurement for Now) |

A Rose by Any Other Name: The Nominal Level of Measurement |

Any Order Is Fine With Me: The Ordinal Level of Measurement |

1 + 1 = 2: The Interval Level of Measurement |

Can Anyone Have Nothing of Anything? The Ratio Level of Measurement |

In Sum . . . |

Using the Computer to Compute Descriptive Statistics |

The SPSS Output |

Real-World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

Why Understanding Variability Is Important |

Computing the Range |

Computing the Standard Deviation |

Why n - 1? What’s Wrong With Just n? |

What’s the Big Deal? |

Computing the Variance |

The Standard Deviation Versus the Variance |

Using the Computer to Compute Measures of Variability |

The SPSS Output |

More SPSS Output |

Real-World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

Why Illustrate Data? |

Ten Ways to a Great Figure (Eat Less and Exercise More?) |

First Things First: Creating a Frequency Distribution |

The Classiest of Intervals |

The Plot Thickens: Creating a Histogram |

The Tallyho Method |

The Next Step: A Frequency Polygon |

Cumulating Frequencies |

Other Cool Ways to Chart Data |

Bar Charts |

Column Charts |

Line Charts |

Pie Charts |

Using the Computer (SPSS, That Is) to Illustrate Data |

Creating a Histogram |

Creating a Bar Graph |

Creating a Line Graph |

Creating a Pie Chart |

Real-World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

What Are Correlations All About? |

Types of Correlation Coefficients: Flavor 1 and Flavor 2 |

Computing a Simple Correlation Coefficient |

A Visual Picture of a Correlation: The Scatterplot |

Bunches of Correlations: The Correlation Matrix |

Understanding What the Correlation Coefficient Means |

Using-Your-Thumb (or Eyeball) Method |

A Determined Effort: Squaring the Correlation Coefficient |

As More Ice Cream Is Eaten . . . the Crime Rate Goes Up (or Association vs. Causality) |

Using SPSS to Compute a Correlation Coefficient |

Creating a Scatterplot (or Scattergram or Whatever) |

Other Cool Correlations |

Parting Ways: A Bit About Partial Correlation |

Using SPSS to Compute Partial Correlations |

Real-World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

An Introduction to Reliability and Validity |

What’s Up With This Measurement Stuff? |

Reliability: Doing It Again Until You Get It Right |

Test Scores: Truth or Dare? |

Observed Score = True Score + Error Score |

Different Types of Reliability |

Test–Retest Reliability |

Parallel Forms Reliability |

Internal Consistency Reliability |

Interrater Reliability |

How Big Is Big? Finally: Interpreting Reliability Coefficients |

And If You Can’t Establish Reliability . . . Then What? |

Just One More Thing |

Validity: Whoa! What Is the Truth? |

Different Types of Validity |

Content Validity |

Criterion Validity |

Construct Validity |

And If You Can’t Establish Validity . . . Then What? |

A Last Friendly Word |

Validity and Reliability: Really Close Cousins |

Real-World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

So You Want to Be a Scientist . . . |

Samples and Populations |

The Null Hypothesis |

The Purposes of the Null Hypothesis |

The Research Hypothesis |

The Nondirectional Research Hypothesis |

The Directional Research Hypothesis |

Some Differences Between the Null Hypothesis and the Research |

Hypothesis |

What Makes a Good Hypothesis? |

Real-World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

Why Probability? |

The Normal Curve (a.k.a. the Bell-Shaped Curve) |

Hey, That’s Not Normal! |

More Normal Curve |

Our Favorite Standard Score: The z Score |

What z Scores Represent |

What z Scores Really Represent |

Hypothesis Testing and z Scores: The First Step |

Using SPSS to Compute z Scores |

Fat and Skinny Frequency Distributions |

Average Value |

Variability |

Skewness |

Kurtosis |

Real-World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

The Concept of Significance |

If Only We Were Perfect |

The World’s Most Important Table (for This Semester Only) |

More About Table 9.1 |

Back to Type I Errors |

Significance Versus Meaningfulness |

An Introduction to Inferential Statistics |

How Inference Works |

How to Select What Test to Use |

Here’s How to Use the Chart |

An Introduction to Tests of Significance |

How a Test of Significance Works: The Plan |

Here’s the Picture That’s Worth a Thousand Words |

Be Even More Confident |

Real-World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

Introduction to the One-Sample Z-Test |

The Path to Wisdom and Knowledge |

Computing the Z-Test Statistic |

So How Do I Interpret z = 2.38, p < .05? |

Using SPSS to Perform a Z-Test |

Special Effects: Are Those Differences for Real? |

Understanding Effect Size |

Real-World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

Introduction to the t-Test for Independent Samples |

The Path to Wisdom and Knowledge |

Computing the t-Test Statistic |

Time for an Example |

So How Do I Interpret t(58) = –0.14, p > .05? |

The Effect Size and t(ea) for Two |

Computing and Understanding the Effect Size |

Two Very Cool Effect Size Calculators |

Using SPSS to Perform a t-Test |

Real-World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

Introduction to the t-Test for Dependent Samples |

The Path to Wisdom and Knowledge |

Computing the t-Test Statistic |

So How Do I Interpret t(24) = 2.45, p < .05? |

Using SPSS to Perform a t-Test |

The Effect Size for t(ea) for Two (Again) |

Real-World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

Introduction to Analysis of Variance |

The Path to Wisdom and Knowledge |

Different Flavors of ANOVA |

Computing the F-Test Statistic |

So How Do I Interpret F(2, 27) = 8.80, p < .05 |

Using SPSS to Compute the F Ratio |

The Effect Size for One-Way ANOVA |

Real-World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

Introduction to Factorial Analysis of Variance |

The Path to Wisdom and Knowledge |

A New Flavor of ANOVA |

The Main Event: Main Effects in Factorial ANOVA |

Even More Interesting: Interaction Effects |

Using SPSS to Compute the F Ratio |

Computing the Effect Size for Factorial ANOVA |

Real-World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

Introduction to Testing the Correlation |

Coefficient |

The Path to Wisdom and Knowledge |

Computing the Test Statistic |

So How Do I Interpret r(28) = .437, p < .05? |

Causes and Associations (Again!) |

Significance Versus Meaningfulness (Again, Again!) |

Using SPSS to Compute a Correlation Coefficient (Again) |

Real-World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

Introduction to Linear Regression |

What Is Prediction All About? |

The Logic of Prediction |

Drawing the World’s Best Line (for Your Data) |

How Good Is Your Prediction? |

Using SPSS to Compute the Regression Line |

The More Predictors the Better? Maybe |

The Big Rule(s) When It Comes to Using Multiple Predictor Variables |

Real-World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

Introduction to Nonparametric Statistics |

Introduction to the Goodness of Fit (One-Sample) Chi-Square |

Computing the Goodness of Fit Chi-Square Test Statistic |

So How Do I Interpret ?2 (2) = 20.6, p < .05? |

Introduction to the Test of Independence Chi-Square |

Computing the Test of Independence Chi-Square Test Statistic |

Using SPSS to Perform Chi-Square Tests |

Goodness of Fit and SPSS |

Test of Independence and SPSS |

Other Nonparametric Tests You Should Know About |

Real-World Stats |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

Multivariate Analysis of Variance |

Repeated Measures Analysis of Variance |

Analysis of Covariance |

Multiple Regression |

Meta-analysis |

Discriminant Analysis |

Factor Analysis |

Path Analysis |

Structural Equation Modeling |

Summary |

What You Will Learn in This Chapter |

Our Sample Data Set—Who Doesn’t Love Babies? |

Counting Outcomes |

Counting With Frequencies |

Pivot Tables and Cross-Tabulation: Finding Hidden Patterns |

Creating a Pivot Table |

Modifying a Pivot Table |

Summary |

Time to Practice |

What You Will Learn in This Chapter |

Selecting the Perfect Statistics Software |

What’s Out There |

First, the Free Stuff |

Time to Pay |

Summary |

What You Will Learn in This Chapter |

How About Studying Statistics in Stockholm |

Who’s Who and What’s Happened? |

It’s All Here |

HyperStat |

Data? You Want Data? |

More and More Resources |

Online Statistical Teaching Materials |

And, of Course, YouTube . . . |

And, Finally . . . |

### Supplements

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I like this text, but I was reviewing it as a supplemental text, and I had mixed feelings about some of the information. So, I had the students to review the text, since they are the ones that will ultimately be using the text for reference. Half the class loved it, and half the class was on the fence... still struggling with some of the concepts even after reviewing the text. I want one text that is broken down to the point that my weakest student can follow it. At this time, I'm tabling this text while I search for something the entire class can associate with. If I do not find anything before next semester, then I may revisit this text at that time, with another class of students.

**Math/Science Dept, Richmond Community College**

Young research students make no apologies when they report they are afraid of statistics. Salkind's text disarms many of the fears with the title and the humorous dialogue, the use of emoticons, and cartoons throughout the text. The plain no-nonsense, step-by-step instruction helps today's student who already struggles with attention span. Now, students possess a resource that they can revisit in their home or dorm room.

**Organization Development, Calvary Bible College Theo Sem**

This text was great and now the only thing I'll use to teach statistics!

**Psychology Dept, SUNY College at Old Westbury**

This text was great and now the only thing I'll use to teach statistics!

**Psychology Dept, SUNY College at Old Westbury**

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