Statistics for the Terrified v6.01: what does it cover?

Statistics for the Terrified covers all the techniques required by undergraduate students and many researchers in a user-friendly, commonsense, non-mathematical and highly interactive way.

Each chapter of the tutorial begins with an outline of its aims and objectives, and finishes with a summary of the key points covered. Click on the links below for screenshots.


Before you begin

  • The trouble with Statistics
  • How this tutorial will help you
  • What is Statistics?
  • The art of detective work


    Probability

  • Aims and objectives
  • Probability
  • Probability notation
  • Getting a feel for conditional probability - Screenshot
  • Joanna Watson: a conditional probability scenario
  • Conditional probability as areas
  • Conditional probability: the calculations
  • Conditional probability
  • Bayes theorem helps untangle confused logic
  • Summary


    Basic numerical data description

  • Aims and objectives
  • Data types
  • Average values - the mean, median introduced - Screenshot
  • The effect of outliers on the mean and median
  • The advantages and disadvantages of the mean and median
  • Range and variance
  • Variance and Standard deviation
  • What is the coefficient of variation?
  • Summary


    Graphical data description

  • Aims and objectives
  • Frequency charts and histograms - Screenshot
  • Histograms and distributions
  • Introducing the normal curve
  • The normal curve shows relative likelihood - Screenshot
  • How sd affects normal curve shape
  • Normal distribution and calculating probabilities: datagame - Screenshot
  • Dealing with data that isn't normally distributed
  • Summary


    Standard error and confidence intervals

  • Aims and objectives
  • Introduction to standard error
  • Standard error measures the accuracy of estimates - Screenshot
  • Standard error decreases as standard deviation increases
  • Accuracy increases as standard error decreases
  • How this looks with normal curves
  • How to calculate standard error in theory
  • How to calculate standard error in practice
  • Isn't it cheating to use estimates in the calculation?
  • Standard error and standard deviation are not the same! Recap
  • Confidence intervals are based on standard error
  • A 95% CI contains the population mean 95% of the time - Screenshot
  • Summary


    What does p<0.05 really mean?

  • Aims and objectives - Screenshot
  • What is hypothesis testing?
  • Posing the hypothesis
  • Introducing the null hypothesis
  • Random sampling
  • What do you take into account? - Screenshot
  • The steps involved in hypothesis testing
  • The relationship between the test statistic and p values
  • The story so far...
  • How difficult is it to reject the null hypothesis?
  • The relationship between power and type II errors
  • Power worsens as the alternative gets closer
  • Making significance tests more powerful
  • Summary


    How to choose a test

  • Aims and objectives
  • The essentials
  • For simple but essential concepts
  • Groups
  • Data patterns - Screenshot
  • Appropriate tests
  • Questions to ask - Screenshot
  • The test quiz
  • Summary


    Testing for differences between groups

  • Aims and objectives
  • Overview - tests for differences between group means - Screenshot
  • Two-sample t test overview
  • Two-sample t test exploration
  • Two-sample t test datagame
  • Analysis of variance (ANOVA) overview
  • Oneway ANOVA: exploration and datagame - Screenshot
  • What is twoway analysis of variance?
  • Twoway ANOVA: interpretation and datagame
  • Non-parametric methods: ranking
  • Mann-Whitney test - datagame and explore
  • Kruskal-Wallis test - datagame and explore
  • Summary


    Analysing repeated measures

  • Aims and objectives
  • What are repeated measures?
  • Collapse the data
  • Before/after tests - Screenshot
  • The paired t test
  • The paired t test datagame - Screenshot
  • Wilcoxon signed rank test overview
  • Wilcoxon signed rank test datagame
  • Why area under a curve?
  • Datagame the areas
  • Dealing with repeated measures: Recap
  • Data patterns
  • Tripping over the truth
  • Time series: smoothing - moving averages
  • Time series: exponential smoothing
  • Summary


    Analysing 2x2 classification tables

  • Aims and objectives
  • What are classification tables?
  • Overview of the use and meaning of classification tables
  • Calculating risk is straightforward
  • Absolute risk and relative risk
  • Testing: introducing the chi-square statistic
  • Interpretation of discrepancies and relative risk - Screenshot
  • The chi-square test, sample size and Fisher's exact test
  • The chi-square test datagame
  • Summary - Screenshot


    Fitting lines to data

  • Aims and objectives
  • The basics of regression: correlation
  • The basics of regression: what is it good for?
  • The basics of regression: describing the line
  • Exploring linear regression
  • Regression: datagame - Screenshot
  • The pulling power of outliers
  • Regression parameters and their standard errors
  • Hypothesis testing a linefit
  • Hypothesis testing challenges
  • Summary


    Uncovering hidden influences

  • Aims and objectives
  • A little knowledge is a dangerous thing 1 - Screenshot
  • A little knowledge is a dangerous thing 2
  • Analysis of covariance: uncovering influence by linefitting 1
  • Analysis of covariance: uncovering influence by linefitting 2
  • Twoway analysis of variance: reducing bias and variance 1
  • Twoway analysis of variance: reducing bias and variance 2
  • Twoway analysis of variance: reducing bias and variance 3
  • Matching and the removal of bias
  • Summary


    • "I have found Statistics for the Terrified to be a valuable tool in helping students to grasp some core concepts in statistical testing such as the standard error, confidence intervals and hypothesis testing. I have found the interactive multi-media element of Statistics for the Terrified to be particularly powerful in this regard."

    Professor Steve Strand
    Institute of Education
    Warwick University


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