#
An Adventure in Statistics
The Reality Enigma

- Andy Field - University of Sussex, UK

**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

**Available with**

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*Perusall*is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective. Learn more.

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 |

2.1. 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 |

3.1. 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 |

4.1. 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 |

5.1. 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 |

6.1. 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 |

7.1. 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 |

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 |

9.1. 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 |

10.1. 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 |

11.1. 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 |

12.1. 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 |

13.1. 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 |

14.1. 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 |

15.1. 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 |

16.1. 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 |

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 |

**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!”

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“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.”

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“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.”

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“Funny and engaging book, YOU CANNOT STOP READING it because you cannot wait to know what happens to Zach and Alice.”

**Bangor University**

“This book is EXTREMELY HELPFUL IN UNDERSTANDING THE BASICS behind some of the more complex statistical procedures gone over in lectures.”

**Bangor University**