An Adventure in Statistics
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An Adventure in Statistics : The Reality Enigma

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Description

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...


Author News




"Eight years ago I had the idea to write a fictional story through which the student learns statistics via a shared adventure with the main character..." Read the complete article from Andy Field on writing his new book
Times Higher Education article: "Andy Field takes statistics adventure to a new level"


Stay Connected


Connect with us on Facebook and share your experiences with Andy's texts, check out news, access free stuff, see photos, watch videos, learn about competitions, and much more.


Video Links


Go behind the scenes and learn more about the man behind the book:




Watch Andy talk about why he created a statistics book using the framework of a novel and illustrations by one of the illustrators for the show, Doctor Who.
See more videos on Andy's YouTube channel
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Product details

  • Paperback | 768 pages
  • 189 x 246 x 43.18mm | 1,430g
  • London, United Kingdom
  • English
  • 1446210456
  • 9781446210451
  • 3,519

Table of contents

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
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Review quote

It's an excellent resource and successfully makes statistics more accessible [...] the story format encourages readers to persevere and to complete the course. It also gives readers quirky (and therefore memorable) analogies with which to visualize or conceptualise the statistical theories -- Marguerite Adewoye Students who come to the book for statistics learning stay with it for the engaging story. By the end of the book, the reader gets completely satiated not only from the story but also from the abundant concepts learned during the unfolding of the story. The book is highly entertaining, tremendously exciting, meticulously thorough on subject matter coverage and amazingly simple to understand and follow -- Naveen Kashyap "I have at last encountered a book that provides solid, innovative statistics instruction alongside lessons in coding. And it's fair to say that it does so like no other. Andy Field's An Adventure in Statistics: The Reality Enigma-an introductory statistics educational text embedded in a science fiction story with graphic-novel artwork-has caught my attention and kept it. If only I'd had this book back in grad school...As an experienced educator, Field has a good sense of where a student might get held up, and he makes sure to cover such topics repeatedly to emphasize certain points...the fictional story exists in service of the statistics instruction, as the narrative flow is driven by wherever the statistics lessons need to go next...it succeeds in making a normally dry read into one that is fun, emotive, and even suspenseful." -- Katie L. Burke * The Scientific Research Society * "This book is EXTREMELY HELPFUL IN UNDERSTANDING THE BASICS behind some of the more complex statistical procedures gone over in lectures." -- Rebecca, Payne "Funny and engaging book, YOU CANNOT STOP READING it because you cannot wait to know what happens to Zach and Alice." -- Eszter Stockl "A UNIQUE, ENGAGING EXPERIENCE...The narrative helps to build a context for introducing concepts and allows for the characters to explain the concepts through their successive parts. This is a must-have for those studying statistics." -- Benjamin Schade "A MUST-HAVE FOR ANY STATISTICS STUDENT looking for a thorough understanding of statistical terminology and concepts. Students will come for the statistics and stay for the narrative." -- Tess Liddell See what students are saying!


"PERFECT FOR EVERYONE that finds stats difficult, pointless or boring, this book proves them wrong! The way the concepts are explained is so easy to grasp and it makes it even entertaining to learn! I wish I had discovered this sooner!" -- Larissa Quiroga Escamilla
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About Andy Field

Andy Field is Professor of Child Psychopathology at the University of Sussex. He has published over 80 research papers, 29 book chapters, and 17 books mostly on child emotional development and statistics.

He is the founding editor of the Journal of Experimental Psychopathology and has been an associate editor and editorial board member for the British Journal of Mathematical and Statistical Psychology, Cognition and Emotion, Clinical Child and Family Psychology Review and Research Synthesis Methods.

His ability to make statistics accessible and fun has been recognized with local and national teaching awards (University of Sussex, 2001, 2015, 2016; the British Psychological Society, 2007), a prestigious UK National Teaching Fellowship (2010), and the British Psychological Society book award (2006). He adores cats (and dogs), and loves to listen to and play very heavy music. He lives in Brighton with his wonderful wife Zoe and their children.
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Rating details

47 ratings
4.04 out of 5 stars
5 40% (19)
4 34% (16)
3 19% (9)
2 2% (1)
1 4% (2)
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