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An Adventure in Statistics
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An Adventure in Statistics
The Reality Enigma

First Edition
Additional resources:


May 2016 | 768 pages | SAGE Publications Ltd

Shortlisted for the British Psychological Society Book Award 2017
Shortlisted for the British Book Design and Production Awards 2016
Shortlisted for the Association of Learned & Professional Society Publishers Award for Innovation in Publishing 2016

An Adventure in Statistics: The Reality Enigma by best-selling author and award-winning teacher Andy Field offers a better way to learn statistics. It combines rock-solid statistics coverage with compelling visual story-telling to address the conceptual difficulties that students learning statistics for the first time often encounter in introductory courses - guiding students away from rote memorization and toward critical thinking and problem solving. Field masterfully weaves in a unique, action-packed story starring Zach, a character who thinks like a student, processing information, and the challenges of understanding it, in the same way a statistics novice would. Illustrated with stunning graphic novel-style art and featuring Socratic dialogue, the story captivates readers as it introduces them to concepts, eliminating potential statistics anxiety. 

The book assumes no previous statistics knowledge nor does it require the use of data analysis software. It covers the material you would expect for an introductory level statistics course that Field’s other books (Discovering Statistics Using IBM SPSS Statistics and Discovering Statistics Using R) only touch on, but with a contemporary twist, laying down strong foundations for understanding classical and Bayesian approaches to data analysis. 

In doing so, it provides an unrivalled launch pad to further study, research, and inquisitiveness about the real world, equipping students with the skills to succeed in their chosen degree and which they can go on to apply in the workplace.

The Story and Main Characters

The Reality Revolution

In the City of Elpis, in the year 2100, there has been a reality revolution. Prior to the revolution, Elpis citizens were unable to see their flaws and limitations, believing themselves talented and special. This led to a self-absorbed society in which hard work and the collective good were undervalued and eroded.

To combat this, Professor Milton Grey invented the reality prism, a hat that allowed its wearers to see themselves as they really were - flaws and all. Faced with the truth, Elpis citizens revolted and destroyed and banned all reality prisms.

The Mysterious Disappearance

Zach and Alice are born soon after all the prisms have been destroyed. Zach, a musician who doesn’t understand science, and Alice, a geneticist who is also a whiz at statistics, are in love. One night, after making a world-changing discovery, Alice suddenly disappears, leaving behind a song playing on a loop and a file with her research on it.

Statistics to the Rescue!

Sensing that she might be in danger, Zach follows the clues to find her, as he realizes that the key to discovering why Alice has vanished is in her research. Alas! He must learn statistics and apply what he learns in order to overcome a number of deadly challenges and find the love of his life.

As Zach and his pocket watch, The Head, embark on their quest to find Alice, they meet Professor Milton Grey and Celia, battle zombies, cross a probability bridge, and encounter Jig:Saw, a mysterious corporation that might have something to do with Alice’s disappearance…

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Prologue: The Dying Stars
 
1 Why You Need Science: The Beginning and The End
1.1. Will you love me now?

 
1.2. How science works

 
1.2.1. The research process

 
1.2.2. Science as a life skill

 
1.3. Research methods

 
1.3.1. Correlational research methods

 
1.3.2. Experimental research methods

 
1.3.3. Practice, order and randomization

 
1.4. Why we need science

 
 
2 Reporting Research, Variables and Measurement: Breaking the Law
2.1. Writing up research

 
2.2. Maths and statistical notation

 
2.3. Variables and measurement

 
2.3.1. The conspiracy unfolds

 
2.3.2. Qualitative and quantitative data

 
2.3.3. Levels of measurement

 
2.3.4. Measurement error

 
2.3.5. Validity and reliability

 
 
3 Summarizing Data: She Loves Me Not?
3.1. Frequency distributions

 
3.1.1. Tabulated frequency distributions

 
3.1.2. Grouped frequency distributions

 
3.1.3. Graphical frequency distributions

 
3.1.4. Idealized distributions

 
3.1.5. Histograms for nominal and ordinal data

 
3.2. Throwing Shapes

 
 
4 Fitting Models (Central Tendency): Somewhere In The Middle
4.1. Statistical Models

 
4.1.1. From the dead

 
4.1.2. Why do we need statistical models?

 
4.1.3. Sample size

 
4.1.4. The one and only statistical model

 
4.2. Central Tendency

 
4.2.1. The mode

 
4.2.2. The median

 
4.2.3. The mean

 
4.3. The 'fit' of the mean: variance

 
4.3.1. The fit of the mean

 
4.3.2. Estimating the fit of the mean from a sample

 
4.3.3. Outliers and variance

 
4..4. Dispersion

 
4.4.1. The standard deviation as an indication of dispersion

 
4.4.2. The range and interquartile range

 
 
5 Presenting Data: Aggressive Perfector
5.1. Types of graphs

 
5.2. Another perfect day

 
5.3. The art of presenting data

 
5.3.1. What makes a good graph?

 
5.3.2. Bar graphs

 
5.3.3. Line graphs

 
5.3.4. Boxplots (box-whisker diagrams)

 
5.3.5. Graphing relationships: the scatterplot

 
5.3.6. Pie charts

 
 
6 Z-Scores: The wolf is loose
6.1. Interpreting raw scores

 
6.2. Standardizing a score

 
6.3. Using z-scores to compare distributions

 
6.4. Using z-scores to compare scores

 
6.5. Z-scores for samples

 
 
7 Probability: The Bridge of Death
7.1. Probability

 
7.1.1. Classical probability

 
7.1.2. Empirical probability

 
7.2. Probability and frequency distributions

 
7.2.1. The discs of death

 
7.2.2. Probability density functions

 
7.2.3. Probability and the normal distribution

 
7.2.4. The probability of a score greater than x

 
7.2.5. The probability of a score less than x: The tunnels of death

 
7.2.6. The probability of a score between two values: The catapults of death

 
7.3. Conditional probability: Deathscotch

 
 
Inferential Statistics: Going Beyond the Data
8.1. Estimating parameters

 
8.2. How well does a sample represent the population?

 
8.2.1. Sampling distributions

 
8.2.2. The standard error

 
8.2.3. The central limit theorem

 
8.3. Confidence Intervals

 
8.3.1. Calculating confidence intervals

 
8.3.2. Calculating other confidence intervals

 
8.3.3. Confidence intervals in small samples

 
8.4. Inferential statistics

 
 
9 Robust Estimation: Man Without Faith or Trust
9.1. Sources of bias

 
9.1.1. Extreme scores and non-normal distributions

 
9.1.2. The mixed normal distribution

 
9.2. A great mistake

 
9.3. Reducing bias

 
9.3.1. Transforming data

 
9.3.2. Trimming data

 
9.3.3. M-estimators

 
9.3.4. Winsorizing

 
9.3.5. The bootstrap

 
9.4. A final point about extreme scores

 
 
10 Hypothesis Testing: In Reality All is Void
10.1. Null hypothesis significance testing

 
10.1.1. Types of hypothesis

 
10.1.2. Fisher's p-value

 
10.1.3. The principles of NHST

 
10.1.4. Test statistics

 
10.1.5. One- and two-tailed tests

 
10.1.6. Type I and Type II errors

 
10.1.7. Inflated error rates

 
10.1.8. Statistical power

 
10.1.9. Confidence intervals and statistical significance

 
10.1.10. Sample size and statistical significance

 
 
11 Modern Approaches to Theory Testing: A Careworn Heart
11.1. Problems with NHST

 
11.1.1. What can you conclude from a 'significance' test?

 
11.1.2. All-or-nothing thinking

 
11.1.3. NHST is influenced by the intentions of the scientist

 
11.2. Effect sizes

 
11.2.1. Cohen's d

 
11.2.2. Pearson's correlation coefficient,r

 
11.2.3. The odds ratio

 
11.3. Meta-analysis

 
11.4. Bayesian approaches

 
11.4.1. Asking a different question

 
11.4.2. Bayes' theorem revisited

 
11.4.3. Comparing hypothesis

 
11.4.4. Benefits of bayesian approaches

 
 
12 Assumptions: Starblind
12.1. Fitting models: bringing it all together

 
12.2. Assumptions

 
12.2.1. Additivity and linearity

 
12.2.2. Independent errors

 
12.2.3. Homoscedasticity/ homogeneity of variance

 
12.2.4. Normally distributed something or other

 
12.2.5. External variables

 
12.2.6. Variable types

 
12.2.7. Multicollinearity

 
12.2.8. Non-zero variance

 
12.3. Turning ever towards the sun

 
 
13 Relationships: A Stranger's Grave
13.1. Finding relationships in categorical data

 
13.1.1. Pearson's chi-square test

 
13.1.2. Assumptions

 
13.1.3. Fisher's exact test

 
13.1.4. Yates's correction

 
13.1.5. The likelihood ratio (G-test)

 
13.1.6. Standardized residuals

 
13.1.7. Calculating an effect size

 
13.1.8. Using a computer

 
13.1.9. Bayes factors for contingency tables

 
13.1.10. Summary

 
13.2. What evil lay dormant

 
13.3. Modelling relationships

 
13.3.1. Covariance

 
13.3.2. Pearson's correlation coefficient

 
13.3.3. The significance of the correlation coefficient

 
13.3.4. Confidence intervals for r

 
13.3.5. Using a computer

 
13.3.6. Robust estimation of the correlation

 
13.3.7. Bayesian approaches to relationships between two variables

 
13.3.8. Correlation and causation

 
13.3.9. Calculating the effect size

 
13.4. Silent sorrow in empty boats

 
 
14 The General Linear Model: Red Fire Coming Out From His Gills
14.1. The linear model with one predictor

 
14.1.1. Estimating parameters

 
14.1.2. Interpreting regression coefficients

 
14.1.3. Standardized regression coefficients

 
14.1.4. The standard error of b

 
14.1.5. Confidence intervals for b

 
14.1.6. Test statistic for b

 
14.1.7. Assessing the goodness of fit

 
14.1.8. Fitting a linear model using a computer

 
14.1.9. When this fails

 
14.2. Bias in the linear model

 
14.3. A general procedure for fitting linear models

 
14.4. Models with several predictors

 
14.4.1. The expanded linear model

 
14.4.2. Methods for entering predictors

 
14.4.3. Estimating parameters

 
14.4.4. Using a computer to build more complex models

 
14.5. Robust regression

 
14.5.1. Bayes factors for linear models

 
 
15 Comparing Two Means: Rock or Bust
15.1. Testing differences between means: The rationale

 
15.2. Means and the linear model

 
15.2.1. Estimating the model parameters

 
15.2.2. How the model works

 
15.2.3. Testing the model parameters

 
15.2.4. The independent t-test on a computer

 
15.2.5. Assumptions of the model

 
15.3. Everything you believe is wrong

 
15.4. The paired-samples t-test

 
15.4.1. The paired-samples t-test on a computer

 
15.5. Alternative approaches

 
15.5.1. Effect sizes

 
15.5.2. Robust tests of two means

 
15.5.3. Bayes factors for comparing two means

 
 
16 Comparing Several Means: Faith in Others
16.1. General procedure for comparing means

 
16.2. Comparing several means with the linear model

 
16.2.1. Dummy coding

 
16.2.2. The F-ratio as a test of means

 
16.2.3. The total sum of squares (SSt)

 
16.2.4. The model sum of squares (SSm)

 
16.2.5. The residual sum of squares (SSr)

 
16.2.6. Partitioning variance

 
16.2.7. Mean squares

 
16.2.8. The F-ratio

 
16.2.9. Comparing several means using a computer

 
16.3. Contrast coding

 
16.3.1. Generating contrasts

 
16.3.2. Devising weights

 
16.3.3. Contrasts and the linear model

 
16.3.4. Post hoc procedures

 
16.3.5. Contrasts and post hoc tests using a computer

 
16.4. Storm of memories

 
16.5. Repeated-measures designs

 
16.5.1. The total sum of squares, SSt

 
16.5.2. The within-participant variance, SSw

 
16.5.3. The model sum of squares, SSm

 
16.5.4. The residual sum of squares, SSr

 
16.5.5. Mean squares and the F-ratio

 
16.5.6. Repeated-measures designs using a computer

 
16.6. Alternative approaches

 
16.6.1. Effect sizes

 
16.6.2. Robust tests of several means

 
16.6.3. Bayesian analysis of several means

 
16.7. The invisible man

 
 
Factorial Designs
17.1. Factorial designs

 
17.2. General procedure and assumptions

 
17.3. Analysing factorial designs

 
17.3.1. Factorial designs and the linear model

 
17.3.2. The fit of the model

 
17.3.3. Factorial designs on a computer

 
17.4. From the pinnacle to the pit

 
17.5. Alternative approaches

 
17.5.1. Calculating effect sizes

 
17.5.2. Robust analysis of factorial designs

 
17.5.3. Bayes factors for factorial designs

 
17.6. Interpreting interaction effects

 
 
Epilogue: The Genial Night: SI Momentum Requiris, Circumspice

Supplements

Click for online resources

SAGE edge FREE Online Resources / Companion Website 

Designed to enhance each student’s learning experience, SAGE edge features carefully crafted tools and resources that encourage review, practice, and critical thinking to give students the edge they need to master course content. It also gives instructors access to course management solutions that save time and make teaching easier. 

SAGE edge for Instructors supports teaching with quality content, featuring: 

  • Test banks that provide a diverse range of customizable test items, save time, and offer a pedagogically robust way to measure your students’ understanding of the material
  • Editable, chapter-specific PowerPoint® slides featuring the tables and figures from the text to offer flexibility when creating multimedia lectures so you can customize to your exact needs 

SAGE edge for Students helps students accomplish their coursework goals in an easy-to-use, rich online learning environment that offers: 

  • Learning objectives to reinforce the most important material covered in each chapter
  • eFlashcards to strengthen understanding of key terms and concepts
  • Practice quizzes with multiple choice questions to encourage self-guided assessment and exam preparation
  • Datasets and R scripts from each chapter with hands-on exercises and problems that allow students to apply their knowledge and work through the Check your Brain problems and end-of-chapter puzzles in the text
  • Zach’s Facts from each chapter to promote targeted review of key concepts in an easy-to-access online format
  • Answers to end-of-chapter questions to allow students to track their progress
  • An online action plan highlighting all the resources available on the website that includes tips and feedback on progress through the course and materials, which allows students to individualize their learning experience
  • A bit of distraction in the form of fun quizzes and games that offer an energizing break from all that studying  
  • Links to study skills resources that appeal to different learning styles
  • Author videos and social media content designed to enhance student engagement, including access to author videos on YouTube as well as to regularly updated postings on the author’s Facebook and Twitter channels

This book is truly outstanding!!! Perfect read, easy to understand, and above all - really fun!! Andy Shield is a genius and a great novelist as well! This book is even more than I was looking for!!!

Dr Peter M. Bican
Management, FAU Erlangen-Nürnberg
February 3, 2019

This text is indeed an adventure in statistics. Unlike any statistical text I have ever used. By creating a background storyline, Field, makes concepts much easier to grasp. I have not just recommended it to all Postgraduate students who describe themselves as more qualitative in their approach,but also to others who need a review of statistical concepts that will capture their imagination. Highly recommended it.

Dr Roberto Parada
School of Education, Western Sydney University - Penrith Campus
October 28, 2018

This book is accessible and easy to read for students in their research training. The storyline and cartoons make difficult concepts easy to grasp.

Dr Jay Vickers
Health, The University of Worcester
April 9, 2018

Another great book from Andy Fields, making statistics understandable by giving context to the examples

Mrs Carol Ellis
Allied Health & Medicine (Chelmsford), Anglia Ruskin University
April 6, 2018

Reading about statistics was never this exciting. I'm afraid some of the students will not appreciate it since it deviates too much from the standard textbook style so I wouldn't use it as the main course book. However, I'm definitely going to suggest it as an alternative to those who find the regular statistics textbooks too dry. It's also excellent reading for nerdy cat-lover types such as myself.

Dr Tuomo Häikiö
Department of Psychology, University of Turku
August 31, 2017

An essential read for students undertaking quantitative research for their projects. The book outlines information in an easy accessible manner and students enjoy the layout and format so that they can familiarise themselves with the approach they have chosen. Very comprehensive book.

Dr Helen Nicholas
Institute of Health & Society, Worcester University
June 13, 2017

testing review functionality

Ms Beverly Shideler
History, Five Towns College
May 24, 2017

This book is great for students in education disciplines. My students loved the entire book.

Dr Bahadir Namdar
Primary Education/ Science Education Program, Recep Tayyip Erdogan University
May 18, 2017

Brilliant read, makes statistics enjoyable

Ms Bernadette Snow
Environmental Studies, Nelson Mandela Metropolitan University
May 6, 2017

A novel approach to teaching statistics which is sure to engage the students and make it easier for them to grasp these concepts in terms of real-world problems.

Dr Dinesh Ramoo
Psychology, Cag Universitesi
April 6, 2017

Sample Materials & Chapters

Prologue

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5


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