Statistics Quick Reference Card
Module 1: Introduction to Statistics & Data
GOLDEN RULE: Correlation ≠ Causation • Only EXPERIMENTS can prove cause-and-effect!
Two Branches
Descriptive
Summarize data you have
"What happened?"
Inferential
Predict beyond your data
"What can we conclude?"
Data Types
| Quantitative | Qualitative |
|---|---|
| Numbers, can calculate | Categories, labels |
Quantitative Subtypes
Discrete: Count it (0, 1, 2...)
Continuous: Measure it (any value)
Levels of Measurement
| Level | Example |
|---|---|
| Nominal | Eye color, zip codes |
| Ordinal | Rankings, ratings |
| Interval | Temperature (°F) |
| Ratio | Height, weight, age |
Data Collection
| Method | Causation? |
|---|---|
| Observational | No |
| Survey | No |
| Experiment | YES! |
Sampling Methods
| Method | Quality |
|---|---|
| Simple Random | Good |
| Stratified | Good |
| Cluster | Good |
| Systematic | Usually good |
| Convenience | BIASED |
| Voluntary Response | BIASED |
Key Terms
Population: Entire group
Sample: Subset studied
Parameter: Population value
Statistic: Sample value
Graphs
| Type | Use For |
|---|---|
| Bar Chart | Compare categories (gaps) |
| Histogram | Distribution (bars touch) |
| Pie Chart | Parts of whole (100%) |
| Line Graph | Trends over time |
| Scatterplot | Two variables relationship |
Bar chart bars have GAPS
Histogram bars TOUCH
Histogram bars TOUCH
Misleading Graphs
- Truncated y-axis (not starting at 0)
- Cherry-picking time periods
- 3D effects distorting proportions
- Wrong graph type for data
- Manipulated dual axes
Formula: Mean (Average)
Mean = (Sum of all values) ÷ (Number of values)
Critical Thinking Checklist
- How was data collected?
- What's the sample size?
- Is the sample representative?
- Could there be bias?
- Correlation or causation?
- Is the graph misleading?
- What's the source?
- What's missing?
Free Statistics Learning Platform • Safaa Dabagh • sdabagh.github.io • © 2025