An Adventure in Statistics: The Reality Enigma

The Reality Enigma

Andy Field

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An Adventure in Statistics has been shortlisted for the Once again, bestselling author and award-winning teacher Andy Field hasn't just broken the traditional textbook mould with his new novel/textbook, he has forged the only statistics book on the market with a terrifying probability bridge, zombies and a talking cat! Andy Field's unique approach gently introduces students across the social sciences to the importance and relevance of statistics in a stunningly illustrated format and style. By weaving in a compelling narrative, he takes students on an exciting journey through introductory level statistics overcoming potential anxiety around the subject and providing a vibrant alternative to the dullness of many typical offerings. The medium, the message and the rock-solid statistics coverage combine to raise the level of attainment of even the most Maths-phobic student. It assumes no previous knowledge, nor requires the use of data analysis software. It covers the material you would expect for an introductory level statistics module that his previous 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 launchpad 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. Our Facebook page for lovers of Andy Field's books and statistics-phobes alike is a place for readers to share their experiences of Andy's texts and where we post news, free stuff, photos, videos, competitions and more. Join us at


Einband gebundene Ausgabe
Seitenzahl 768
Erscheinungsdatum 16.06.2016
Sprache Englisch
ISBN 978-1-4462-1044-4
Verlag KNV Besorgung
Maße (L/B/H) 24.9/19.2/4.8 cm
Gewicht 1604 g


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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