Week 1 — Readings & Resources · Foundations & Types of Data

Course: Introduction to Statistics (MATH 11) · Silver Oak University (fictional sample) · Prof. Rivera
Objective covered: Objective 1 — Distinguish populations from samples and identify appropriate sampling and study designs.

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.

This week's load is deliberately light: ~4 short readings + ~4 short videos, grouped by the four ideas from the lecture. Read or watch one item per group and you're ready for the quiz; do all of them and you'll be very comfortable. Total time is roughly 45–55 minutes if you do everything, far less if you pick one per group.

Reading order that matches the lecture: ① population/sample & parameter/statistic → ② levels of measurement (NOIR) → ③ sampling & bias → ④ observational vs. experiment / correlation ≠ causation.

A habit to start now: before you trust any item below, you already know the three questions from class — Who was measured? How were they picked? What was actually recorded? Keep them in mind as you read.

① Population vs. Sample · Parameter vs. Statistic

Maps to 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 exact split we drew on the board — population vs. sample, with a worked parameter-vs-statistic example.
⏱ ~6 min

Reading — "Parameter vs. Statistic: Definitions, Differences & Examples" (Scribbr)
🔗 https://www.scribbr.com/statistics/parameter-vs-statistic/
Why it's assigned: nails the one pair students mix up, and previews the notation from lecture (p and p̂, μ and x̄) so the symbols feel familiar later.
⏱ ~6 min

Video — "Identifying a Sample and Population" (Khan Academy)
🔗 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 Segment 2.
⏱ ~4 min

② Levels of Measurement (Nominal · Ordinal · Interval · Ratio)

Maps to Lecture Segment 3. Remember the memory 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" (Scribbr)
🔗 https://www.scribbr.com/methodology/types-of-variables/
Why it's assigned: sorts variables into categorical vs. quantitative and then into all four levels, with everyday examples — the same path we walked in class, including why a zip code is a label, not a number you can average.
⏱ ~6 min

③ How We Pick · Sampling Methods & Bias

Maps to 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 four probability methods (simple random, stratified, cluster, systematic) against the traps (convenience, voluntary response), so the stratified-vs-cluster mix-up finally clicks.
⏱ ~8 min

Reading — "Types of Sampling Methods" (Khan Academy, article)
🔗 https://www.khanacademy.org/math/statistics-probability/designing-studies/sampling-methods-stats/a/sampling-methods-review
Why it's assigned: a tight, example-driven review of the same methods plus why a non-random sample ends up biased — a good second pass if the first reading felt fast.
⏱ ~5 min

Video — "Sampling Methods and Bias with Surveys: Crash Course Statistics #10"
🔗 https://www.youtube.com/watch?v=Rf-fIpB4D50
Why it earns the click: the liveliest tour of good vs. bad surveys, with the bias traps from Segment 6 shown in action.
⏱ ~12 min

④ Observational Study vs. Experiment · Correlation ≠ Causation

Maps to Lecture Segment 7. The line to carry out of this week: correlation is a handshake, not a push — and 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 coffee-and-grades headline doesn't prove cause.
⏱ ~7 min

Video — "Controlled Experiments: Crash Course Statistics #9"
🔗 https://www.youtube.com/watch?v=kkBDa-ICvyY
Why it earns the click: shows what a real experiment adds — random assignment, control, blinding — that an observational study can't, so you see why the arrow needs an experiment.
⏱ ~11 min

Video — "Correlation Doesn't Equal Causation: Crash Course Statistics #8"
🔗 https://www.youtube.com/watch?v=GtV-VYdNt_g
Why it earns the click: the single best 11 minutes on why two things moving together isn't proof one causes the other — pure Segment 7.
⏱ ~11 min

Optional one-stop reference (free online text)

If you'd like one optional reference to skim all term, OpenIntro Statistics keeps its full text and per-section videos free to read online. Chapter 1 ("Intro to Data") covers everything in this week — data basics, sampling, and experiments.
🔗 https://www.openintro.org/book/os/
Why it's here: a reputable, currently-available reference you can return to in later weeks — entirely optional this week.

Pick-one quick path (≈15 min total)

In a hurry? Do exactly these four and you'll be ready for the quiz:
1. Read Population vs. Sample (group ①).
2. Read Types of Variables (group ②).
3. Watch Crash Course #10 — Sampling Methods & Bias (group ③).
4. 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 Prof. Rivera and use the OpenIntro reference above in the meantime.