S&DS 1000
Introductory Statistics — Practice Question Bank
150+ Problems with Fully Worked Solutions
Independent Study Resource
Not affiliated with or endorsed by Yale University
Based on OpenStax Introductory Statistics 2e (CC BY 4.0)
Edition 2026
Explore our developer-friendly HTML to PDF API Printed using PDFCrowd HTML to PDF
,Table of Contents
1. How to Use This Question Bank
2. Chapter 1: Sampling and Data — 10 problems
3. Chapter 2: Descriptive Statistics — 12 problems
4. Chapter 3: Probability Topics — 12 problems
5. Chapter 4: Discrete Random Variables — 10 problems
6. Chapter 5: Continuous Random Variables — 10 problems
7. Chapter 6: The Normal Distribution — 12 problems
8. Chapter 7: The Central Limit Theorem — 10 problems
9. Chapter 8: Confidence Intervals — 12 problems
10. Chapter 9: Hypothesis Testing with One Sample — 12 problems
11. Chapter 10: Hypothesis Testing with Two Samples — 10 problems
12. Chapter 11: The Chi-Square Distribution — 10 problems
13. Chapter 12: Linear Regression and Correlation — 12 problems
14. Chapter 13: F Distribution and One-Way ANOVA — 10 problems
15. Final Mixed Practice Exam — 25 problems
16. Full Answer Key with Worked Solutions
17. Quick Formula Reference
Explore our developer-friendly HTML to PDF API Printed using PDFCrowd HTML to PDF
,How to Use This Question Bank
This question bank complements the S&DS 1000 Complete Study Guide. Where the guide teaches the
concepts, this bank trains you to execute them under exam conditions.
Recommended workflow
1. Attempt each problem without looking at the solution. Set a timer: 3–5 minutes per
computational problem, 6–10 minutes for applied problems.
2. Check your method, not just your answer. Compare against the worked solution.
3. Re-do missed problems after 48 hours. Spaced repetition is the strongest retention tool.
4. Use the final mixed exam last. It integrates everything under one time limit.
Difficulty Legend
[C] Computational — apply a formula directly.
[A] Applied — translate a real-world scenario.
[X] Conceptual — test understanding.
[★] Challenge — exam-level, multi-step.
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, Chapter 1 — Sampling and Data
Problems
1.1 [X] Distinguish between a parameter and a statistic with examples.
1.2 [A] A researcher selects 10 students from each of 5 randomly chosen classes. Identify the sampling
method.
1.3 [X] Explain why random assignment is important in experiments.
1.4 [C] Classify each variable as quantitative or qualitative: (a) height, (b) eye color, (c) number of
siblings, (d) temperature.
1.5 [C] Classify each quantitative variable as discrete or continuous: (a) number of cars in a parking lot,
(b) weight of a package, (c) number of students in a class.
1.6 [A] A study surveys only people who visit a gym. What type of bias does this introduce?
1.7 [X] Distinguish between descriptive and inferential statistics with examples.
1.8 [A] A hospital wants to test whether a new drug reduces recovery time. Describe a simple
experimental design, including control and treatment groups.
1.9 [★] A sample of 500 voters finds that 55% support a candidate. Identify the sample, the
population, the statistic, and the parameter.
1.10 [★] Explain why a convenience sample can produce reliable results for some research questions
but not others.
Worked Solutions
1.1 Parameter: population summary (e.g., population mean μ). Statistic: sample summary (e.g., sample mean
x̄).
1.2 Cluster sample (entire classes selected).
1.3 It minimizes pre-existing differences between groups, allowing causal inference.
1.4 (a) Quantitative, (b) Qualitative, (c) Quantitative, (d) Quantitative.
1.5 (a) Discrete, (b) Continuous, (c) Discrete.
1.6 Selection bias — non-gym-goers are excluded.
Explore our developer-friendly HTML to PDF API Printed using PDFCrowd HTML to PDF
Introductory Statistics — Practice Question Bank
150+ Problems with Fully Worked Solutions
Independent Study Resource
Not affiliated with or endorsed by Yale University
Based on OpenStax Introductory Statistics 2e (CC BY 4.0)
Edition 2026
Explore our developer-friendly HTML to PDF API Printed using PDFCrowd HTML to PDF
,Table of Contents
1. How to Use This Question Bank
2. Chapter 1: Sampling and Data — 10 problems
3. Chapter 2: Descriptive Statistics — 12 problems
4. Chapter 3: Probability Topics — 12 problems
5. Chapter 4: Discrete Random Variables — 10 problems
6. Chapter 5: Continuous Random Variables — 10 problems
7. Chapter 6: The Normal Distribution — 12 problems
8. Chapter 7: The Central Limit Theorem — 10 problems
9. Chapter 8: Confidence Intervals — 12 problems
10. Chapter 9: Hypothesis Testing with One Sample — 12 problems
11. Chapter 10: Hypothesis Testing with Two Samples — 10 problems
12. Chapter 11: The Chi-Square Distribution — 10 problems
13. Chapter 12: Linear Regression and Correlation — 12 problems
14. Chapter 13: F Distribution and One-Way ANOVA — 10 problems
15. Final Mixed Practice Exam — 25 problems
16. Full Answer Key with Worked Solutions
17. Quick Formula Reference
Explore our developer-friendly HTML to PDF API Printed using PDFCrowd HTML to PDF
,How to Use This Question Bank
This question bank complements the S&DS 1000 Complete Study Guide. Where the guide teaches the
concepts, this bank trains you to execute them under exam conditions.
Recommended workflow
1. Attempt each problem without looking at the solution. Set a timer: 3–5 minutes per
computational problem, 6–10 minutes for applied problems.
2. Check your method, not just your answer. Compare against the worked solution.
3. Re-do missed problems after 48 hours. Spaced repetition is the strongest retention tool.
4. Use the final mixed exam last. It integrates everything under one time limit.
Difficulty Legend
[C] Computational — apply a formula directly.
[A] Applied — translate a real-world scenario.
[X] Conceptual — test understanding.
[★] Challenge — exam-level, multi-step.
Explore our developer-friendly HTML to PDF API Printed using PDFCrowd HTML to PDF
, Chapter 1 — Sampling and Data
Problems
1.1 [X] Distinguish between a parameter and a statistic with examples.
1.2 [A] A researcher selects 10 students from each of 5 randomly chosen classes. Identify the sampling
method.
1.3 [X] Explain why random assignment is important in experiments.
1.4 [C] Classify each variable as quantitative or qualitative: (a) height, (b) eye color, (c) number of
siblings, (d) temperature.
1.5 [C] Classify each quantitative variable as discrete or continuous: (a) number of cars in a parking lot,
(b) weight of a package, (c) number of students in a class.
1.6 [A] A study surveys only people who visit a gym. What type of bias does this introduce?
1.7 [X] Distinguish between descriptive and inferential statistics with examples.
1.8 [A] A hospital wants to test whether a new drug reduces recovery time. Describe a simple
experimental design, including control and treatment groups.
1.9 [★] A sample of 500 voters finds that 55% support a candidate. Identify the sample, the
population, the statistic, and the parameter.
1.10 [★] Explain why a convenience sample can produce reliable results for some research questions
but not others.
Worked Solutions
1.1 Parameter: population summary (e.g., population mean μ). Statistic: sample summary (e.g., sample mean
x̄).
1.2 Cluster sample (entire classes selected).
1.3 It minimizes pre-existing differences between groups, allowing causal inference.
1.4 (a) Quantitative, (b) Qualitative, (c) Quantitative, (d) Quantitative.
1.5 (a) Discrete, (b) Continuous, (c) Discrete.
1.6 Selection bias — non-gym-goers are excluded.
Explore our developer-friendly HTML to PDF API Printed using PDFCrowd HTML to PDF
