Week 1 — Quiz (auto-graded) · Foundations & Types of Data

Course: Introduction to Statistics (MATH 11) · Silver Oak University (fictional sample) · Prof. Rivera
Objective tested: Objective 1 — populations vs. samples; sampling & study design (plus levels of measurement).
Points: 10 (1 each) · Assignment group: Quizzes (15% of grade) · Due: end of Module 1.

This is the human-readable quiz with its vetted answer key and feedback. The import-ready Classic QTI is in F-quiz-week-01-qti.xml; the reusable item-bank entries and the Canvas placement block are at the bottom of this file.

Blueprint

No trick questions; distractors target the Week 1 misconceptions named in the lecture outline.

Questions, key, and feedback

Q1 (MC). A researcher wants to know the average student-loan debt of all 18,000 undergraduates at Silver Oak University. She surveys 500 of them. What is the population?
- A. The 500 students surveyed
- B. All 18,000 undergraduates ✅
- C. The average debt figure
- D. Undergraduates who have any debt
Feedback: The population is everyone the question is about — all 18,000. The 500 are the sample. (Distractor A = the classic sample/population swap.)

Q2 (MC). Those 500 students had an average debt of $12,400. The value $12,400 is a —
- A. Parameter
- B. Statistic ✅
- C. Population
- D. Census
Feedback: It comes from the sample, so it's a statistic. The matching population figure would be a parameter. (P→P, S→S.)

Q3 (Multiple answer — select all that apply). Which of the following are categorical variables?
- A. Eye color ✅
- B. Number of siblings
- C. ZIP code ✅
- D. Temperature in °F
- E. Letter grade (A–F) ✅
Feedback: Categorical = labels/groups (eye color, ZIP code, letter grade). Number of siblings and °F are quantitative. (ZIP code looks numeric but labels — a named trap.)

Q4 (MC). A survey records each student's T-shirt size (S / M / L / XL). This variable is measured at the ___ level.
- A. Nominal
- B. Ordinal ✅
- C. Interval
- D. Ratio
Feedback: The categories are ordered (S < M < L < XL) but the gaps aren't equal/measurable → ordinal.

Q5 (MC). Which variable is measured at the ratio level?
- A. Temperature in °C
- B. Calendar year (e.g., 2026)
- C. Number of cups of coffee a student drank yesterday ✅
- D. Jersey number
Feedback: Counts have a true zero (0 = none) and equal gaps → ratio. °C and calendar year are interval (no true zero); jersey number is nominal.

Q6 (Matching). Match each sampling method to its description.
| Method | Correct description |
|---|---|
| Simple random sample | Every individual is equally likely — like names in a hat |
| Stratified | Split into groups, then randomly sample within each group |
| Cluster | Randomly pick whole groups and measure everyone in them |
| Systematic | Order the list and take every k-th individual |
Feedback: The classic mix-up is stratified (sample within every group) vs. cluster (take whole groups).

Q7 (MC). A sports website asks visitors, "Should the city fund a new stadium?" and 8,000 click to vote. This result is most threatened by —
- A. Voluntary-response bias ✅
- B. It is a census of city residents
- C. It is a simple random sample
- D. No bias, because 8,000 is a large number
Feedback: Respondents opted in and sports-site visitors aren't typical residents; large size doesn't fix that. (D = the "bigger is better" trap.)

Q8 (True / False). "Increasing the sample size will correct for a biased sampling method."
- True
- False ✅
Feedback: False. Bias is baked into the method; a bigger biased sample is just confidently wrong (Literary Digest, 1936). Method beats size.

Q9 (MC). Researchers randomly assign some students to use a new tutoring app and others to use nothing, then compare test scores. This is —
- A. An observational study
- B. An experiment ✅
- C. A survey
- D. A census
Feedback: A treatment was deliberately imposed with random assignment → experiment (the only design that can support causation).

Q10 (MC). A study finds students who drink more coffee tend to have higher GPAs. No treatment was assigned. The best conclusion is —
- A. Coffee causes higher GPAs
- B. Higher GPAs cause coffee drinking
- C. There is an association, but a confounding variable could explain both ✅
- D. There is no relationship between coffee and GPA
Feedback: Observational data show a link, not a cause; a confounder (e.g., hours studying) could drive both. Correlation is a handshake, not a push.

Answer key (quick reference)

Quality gate (self-checked): each single-answer item has exactly one correct option; the multiple-answer item lists all three categorical variables; no item asserts a fact outside the Week 1 course definitions. No computation in this quiz, so no arithmetic to mis-key.

Item-bank entries (for variants + the midterm/final)

All ten items are tagged course=MATH11 · week=1 · objective=1 · topic=foundations-types-of-data and deposited in Item Bank: Week 1 — Foundations & Types of Data. The midterm (Week 8) and the per-term variant updates draw fresh items from this bank. (Tags: q1 population-sample, q2 parameter-statistic, q3 categorical-quantitative, q4 ordinal, q5 ratio, q6 sampling-methods, q7 bias, q8 bias-misconception, q9 study-design, q10 causation.)

Canvas placement block

canvas_object   = Quizzes::Quiz
title           = "Week 1 Quiz — Foundations & Types of Data"
assignment_group = "Quizzes"
points_possible = 10
grading_type    = points
due_offset_days = 6        # 6 days after module start
published       = true
shuffle_answers = true
provenance      = "~ Prof. Rivera's edition · Fall 2026 · built with thecoursemaker.com"