Week 1 — Readings & Resources · Statistics, Data & Study Design
Course: Introduction to Statistics (18-week generic edition)
Objective covered: Objective 1 — Distinguish populations from samples and identify appropriate sampling and study designs.
Your primary reading is Chapter 1 (in this module). Everything below is the optional, go-deeper layer.
How to use this page
Everything here is a link to an external resource — open it in your browser, the same way you'd open a YouTube link. Nothing needs to be downloaded, and nothing costs money.
The load is deliberately light: 4 short readings + 4 short videos, grouped by the four big ideas of the week. Read or watch one item per group and you're well prepared; do all of them and you'll be very comfortable. Total time is roughly 50–60 minutes if you do everything, far less if you pick one per group.
Order that matches the chapter and lecture: ① population/sample & parameter/statistic → ② variable types & levels of measurement → ③ sampling & bias → ④ observational vs. experiment / correlation ≠ causation.
A habit to start now: before you trust any statistic you meet in these resources (or anywhere), ask the week's three questions — Who was measured? How were they picked? What was actually recorded?
① Population vs. Sample · Parameter vs. Statistic
Maps to Chapter 1, Section 1 and Lecture Segment 2. The whole course is the bridge from a statistic (what you measured) to a parameter (what you wanted to know).
Reading — Population vs. Sample: Definitions, Differences & Examples (Scribbr)
🔗 https://www.scribbr.com/methodology/population-vs-sample/
Why it's assigned: the cleanest plain-language version of the split, with a worked parameter-vs-statistic example that mirrors ours.
⏱ ~6 min
Video — Identifying a sample and population (Khan Academy) (video, ~4 min, captioned)
🔗 https://www.youtube.com/watch?v=VPM84_yfx5Q
Why it earns the click: a quick worked example of deciding what counts as the population vs. the sample — exactly the judgment call from the transit-survey example.
② Variable Types & Levels of Measurement
Maps to Chapter 1, Section 2 and Lecture Segment 3. Remember the hook: N–O–I–R, and the one test that settles interval vs. ratio — does zero mean "none"?
Reading — Types of Variables in Research & Statistics: Examples (Scribbr)
🔗 https://www.scribbr.com/methodology/types-of-variables/
Why it's assigned: sorts variables into categorical vs. quantitative — including the nominal/ordinal split — with everyday examples; pair it with the chapter's interval-vs-ratio test for the full N-O-I-R picture.
⏱ ~6 min
③ How We Pick · Sampling Methods & Bias
Maps to Chapter 1, Sections 3–4 and Lecture Segments 5–6. The lesson that sticks: method beats size — a huge sample drawn the wrong way is confidently wrong.
Reading — Sampling Methods: Types, Techniques & Examples (Scribbr)
🔗 https://www.scribbr.com/methodology/sampling-methods/
Why it's assigned: lines up the probability methods (simple random, stratified, cluster, systematic) against the traps (convenience, voluntary response), so the stratified-vs-cluster mix-up finally clicks.
⏱ ~8 min
Video — Sampling Methods and Bias with Surveys: Crash Course Statistics #10 (video, ~12 min, captioned)
🔗 https://www.youtube.com/watch?v=Rf-fIpB4D50
Why it earns the click: the liveliest tour of good vs. bad surveys, with the week's bias traps shown in action.
④ Observational Study vs. Experiment · Correlation ≠ Causation
Maps to Chapter 1, Section 5 and Lecture Segment 7. The line to carry out of this week: correlation is a handshake, not a push — only a randomized experiment can support a cause-and-effect claim.
Reading — Correlation vs. Causation: Difference, Designs & Examples (Scribbr)
🔗 https://www.scribbr.com/methodology/correlation-vs-causation/
Why it's assigned: explains the third-variable (confounding) problem with clean examples — the exact reason the fitness-tracker headline doesn't prove cause.
⏱ ~7 min
Video — Controlled Experiments: Crash Course Statistics #9 (video, ~11 min, captioned)
🔗 https://www.youtube.com/watch?v=kkBDa-ICvyY
Why it earns the click: shows what a real experiment adds — random assignment, control groups, blinding — so you see why the causal arrow needs an experiment.
Video — Correlation Doesn't Equal Causation: Crash Course Statistics #8 (video, ~11 min, captioned)
🔗 https://www.youtube.com/watch?v=GtV-VYdNt_g
Why it earns the click: the single best eleven minutes on why two things moving together isn't proof that one causes the other.
Optional one-stop reference (free online text)
If you'd like one optional reference to skim all term, OpenStax Introductory Statistics 2e keeps its full text free to read online. Chapter 1 (Introduction) covers this week end to end — data basics, sampling, and experiments.
🔗 https://openstax.org/books/introductory-statistics-2e/pages/1-introduction
Why it's here: a reputable, currently-available reference you can return to in later weeks — entirely optional this week.
Pick-one quick path (≈20 min total)
In a hurry? You've read Chapter 1 — then do exactly these three and you'll be ready for the quiz:
1. Read Population vs. Sample (group ①).
2. Watch Crash Course #10 — Sampling Methods and Bias (group ③).
3. Watch Crash Course #8 — Correlation Doesn't Equal Causation (group ④).
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 OpenStax reference above in the meantime.