How to use this: open the walkthrough first and go at your own pace with the arrow keys or Next. Then open the Lab: play with the explorer, then practice in the Problem Trainer, using the hints and the animated worked solutions. Use the tools bar at the top of each page to pause the animations, slow them down, have the text read aloud, or get a written description of each picture.
Module 1 Sampling and data
Populations and samples, types of data, study design and the sampling methods that make data trustworthy.
- Population vs sample
- Parameter vs statistic
- Types of variables
- Levels of measurement
- Simple random sampling
- Stratified and cluster
- Systematic and convenience
- Bias
- Observational vs experiment
Module 2 Describing data: graphs, center and spread
Turning raw numbers into graphs and summaries: shape, center, spread and outliers.
- Frequency tables
- Histograms
- Stem-and-leaf
- Box plots
- Mean, median, mode
- Skew
- Range and IQR
- Standard deviation
- Outliers
- Percentiles
Module 3 Probability
The rules of chance with coins, dice, cards, Venn diagrams and tree diagrams.
- Sample spaces
- Addition rule
- Mutually exclusive
- Complement
- Multiplication rule
- Independence
- Conditional probability
- Two-way tables
- Tree diagrams
Module 4 Discrete random variables and the binomial
Random variables, expected value and the binomial distribution.
- Random variables
- Probability distributions
- Expected value
- Standard deviation
- Binomial setting
- Binomial probabilities
- Cumulative probability
- Mean and SD of binomial
Module 5 The normal distribution and z-scores
The bell curve, z-scores, areas under the curve and the empirical rule.
- Density curves
- Normal distribution
- Empirical rule
- z-scores
- Standard normal table
- Area to the left and right
- Percentiles
- Inverse normal
Module 6 Sampling distributions and the Central Limit Theorem
Why sample means behave so well: sampling distributions and the Central Limit Theorem.
- Sampling variability
- Sampling distribution
- Standard error
- Central Limit Theorem
- Sample size effect
- Sample proportions
Module 7 Confidence intervals
Estimating the unknown with a margin of error, and what 95 percent confidence really means.
- Point estimates
- Margin of error
- z intervals
- t intervals
- Degrees of freedom
- Proportion intervals
- Interpreting confidence
- Sample size planning
Module 8 Hypothesis testing with one sample
Claims, evidence and p-values: the logic of a one-sample test.
- Null and alternative
- Test statistic
- p-value
- Significance level
- Type I and Type II errors
- One- and two-tailed
- z test
- t test
- Proportion test
- Power
Module 9 Comparing two groups
Comparing two groups: independent samples, paired data and two proportions.
- Independent samples
- Two-sample t test
- Paired t test
- Two proportions
- Pooled proportion
- Confidence interval for a difference
- A/B tests
Module 10 Chi-square tests
Counts in categories: goodness of fit, independence and homogeneity.
- Observed vs expected
- Chi-square statistic
- Degrees of freedom
- Goodness of fit
- Two-way tables
- Test of independence
- Homogeneity
- Expected count condition
Module 11 Correlation and linear regression
Scatterplots, correlation, the least-squares line and residuals.
- Scatterplots
- Correlation r
- Least-squares line
- Slope and intercept
- Prediction
- Residuals
- r squared
- Extrapolation
- Correlation vs causation
Module 12 ANOVA: comparing many groups
Comparing three or more group means with one-way ANOVA.
- Between vs within variation
- Sum of squares
- Mean squares
- F statistic
- F distribution
- ANOVA table
- Assumptions
- Post hoc comparisons
Module 13 Statistics in health science: risk, odds and screening tests
The numbers behind health research: risk, odds, treatment effects and screening tests.
- Prevalence vs incidence
- Relative risk
- Odds ratio
- Absolute risk reduction
- Number needed to treat
- Sensitivity and specificity
- PPV and NPV
- Study designs
- Confounding
- Forest plots