Week 1 — Readings & Resources
Your primary reading is Chapter 1 (in this module). Everything below is the optional, go-deeper layer — pick what helps, skip the rest.
How to use this page
This page has 12 items (6 readings and 6 videos). Allow 98–112 minutes for everything. Each group points back to the matching chapter section and class-session segment, so use the full menu when you want more practice, or take the quick path below.
Every video here has captions: turn them on with the CC button in the player. The chapter covers the same ideas in text.
Pick-one quick path (20–22 min total)
- Census and sample — Australian Bureau of Statistics
- Introduction to Statistics: Observational Studies and Experiments — Wolfram U
- Sampling Methods and Bias with Surveys: Crash Course Statistics #10 — CrashCourse
① Who was measured? Populations, samples, and the numbers that describe them
Chapter 1, Section “Population and sample: tell the whole from the part” · Session 1, Segment 2, “Populations, samples, parameters, and statistics” · Remember: a statistic comes from the sample you measured and estimates a parameter of the whole population.
- What Is Statistics: Crash Course Statistics #1 — CrashCourse — a lively tour of what statistics is for: asking a question, gathering data, and reaching a conclusion that admits its uncertainty. (video, 13 min) (captions: creator)
- Sample and Population in Statistics — MarinStatsLectures — a calm walk-through of how a sample stands in for a population, with everyday examples. (video, 9 min) (captions: creator)
- Census and sample — Australian Bureau of Statistics — a plain guide from a national statistics agency. Read the parts on a census versus a sample and the table of pros and cons. Note how sample results are used to estimate the whole population: that is the link between a statistic and a parameter. Use it while working through the Week-1 module overview. (reading, 4–6 min)
② What kind of data is it? Variable types and levels of measurement
Chapter 1, Section “Types of data: sort every variable before you summarize it” · Session 1, Segment 3, “Types of variables and levels of measurement” · Remember: the level of measurement decides which summaries make sense, so sort every variable first.
- Variables and Types of Variables — MarinStatsLectures — worked examples of sorting variables into categorical and quantitative. (video, 13 min) (captions: creator)
- Nominal, Ordinal, Interval & Ratio Data: Simple Explanation With Examples — Grad Coach — the four levels of measurement, each with everyday examples you can check against the chapter's table. (video, 11 min) (captions: creator)
- Nominal, Ordinal, Interval, and Ratio Scales — Statistics By Jim — read each scale's section through the summary; stop there, because next week takes up the tables and graphs each type of variable gets. (reading, 8–11 min)
- Variables — Australian Bureau of Statistics — a short, plain page from a national statistics agency that sorts variables into numeric ones, which are quantitative and either continuous or discrete, and categorical ones, which are qualitative and either ordinal or nominal; the flowchart at the end sums it up. (reading, 3–4 min)
③ How were they chosen, and what can the study prove? Sampling and study design
Chapter 1, Section “Sampling methods: choose who gets measured, and watch for bias” · Session 2, Segment 1, “Sampling methods and bias” · Remember: how a sample is chosen matters more than how big it is, and a bigger sample does not fix bias.
- Sampling Methods and Bias with Surveys: Crash Course Statistics #10 — CrashCourse — how random samples earn their trust, and how self-selected samples tilt a survey. (video, 12 min) (captions: creator)
- Introduction to Statistics: Observational Studies and Experiments — Wolfram U — a four-minute contrast between watching and assigning, and why random assignment lets you talk about cause. (video, 4 min) (captions: creator)
- Sampling Methods In Research: Types, Techniques, & Examples — Simply Psychology — a plain guide that gives each method a strength and a weakness; read the sections on random, stratified, opportunity, systematic, cluster, and volunteer sampling, where an opportunity sample is another name for a convenience sample, and skip quota sampling and everything after it. (reading, 6–9 min)
- Data Collection Methods — Stat Trek — a short page that ends on the key point: random assignment is what supports a cause-and-effect conclusion. (reading, 3–4 min)
Optional one-stop reference (free online reference)
- Probability sampling — Statistics Canada, Statistics: Power from Data! — a national statistics agency's own guide; read the introduction and the parts on simple random, systematic, stratified, and cluster sampling, and skip the later sampling designs. (reading, 12–16 min)
Heads-up (links rot): these point to outside sites that occasionally move or rename pages. If a link ever fails, tell your instructor and use the reference above in the meantime.
Provenance: Built with The Course Maker · samples.thecoursemaker.com