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.

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

- 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

- 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

- 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

- 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

- 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

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

- 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

- 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

- 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

- 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

- 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

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