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An Adventure in Statistics
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An Adventure in Statistics
The Reality Enigma

Experience with SAGE edge


© 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

SAGE Edge

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

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
Bangor University

“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
Cardiff Metropolitan University

“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
Youngstown State University

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

Eszter Stockl
Bangor University

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

Rebecca, Payne
Bangor University

Sample Materials & Chapters

Prologue

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5


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ISBN: 9781446210451
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