Question 1
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
Statistical Reasoning Beginner-Intermediate Worksheet: Focus on common variations practice Statistical Reasoning BEGINNER INTERMEDIATE
Level up your Statistical Reasoning skills! You're at Worksheet 4 of 10 (33% through this series). This step-up challenge worksheet features 20 beginner-intermediate-level problems with a focus on common variations practice. Topics covered: statistical reasoning for competitive exams, how to solve statistical reasoning, statistical reasoning tricks.
What you'll learn in this worksheet:
- Understand the logic behind how to solve statistical reasoning
- Learn step-by-step approaches to common variations practice
- Bridge the gap between basic and advanced concepts
- Handle problems with increasing complexity
- Master statistical reasoning for competitive exams through focused practice
Your progress through Statistical Reasoning
Worksheet 4 of 10 (33% complete)
Question 2
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
Question 3
You test positive for a rare disease (1 in 10,000 prevalence). The test is 99% accurate (1% false positive rate).
What is the approximate probability you actually have the disease?
With 10,000 people: 1 true case, but 100 false positives (1% of 9,999). So probability = 1/(1+100) ≈ 1%. This tests base rate neglect.
Question 4
You test positive for a rare disease (1 in 10,000 prevalence). The test is 99% accurate (1% false positive rate).
What is the approximate probability you actually have the disease?
With 10,000 people: 1 true case, but 100 false positives (1% of 9,999). So probability = 1/(1+100) ≈ 1%. This tests base rate neglect.
Question 5
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
Question 6
You test positive for a rare disease (1 in 10,000 prevalence). The test is 99% accurate (1% false positive rate).
What is the approximate probability you actually have the disease?
With 10,000 people: 1 true case, but 100 false positives (1% of 9,999). So probability = 1/(1+100) ≈ 1%. This tests base rate neglect.
Question 7
You test positive for a rare disease (1 in 10,000 prevalence). The test is 99% accurate (1% false positive rate).
What is the approximate probability you actually have the disease?
With 10,000 people: 1 true case, but 100 false positives (1% of 9,999). So probability = 1/(1+100) ≈ 1%. This tests base rate neglect.
Question 8
You test positive for a rare disease (1 in 10,000 prevalence). The test is 99% accurate (1% false positive rate).
What is the approximate probability you actually have the disease?
With 10,000 people: 1 true case, but 100 false positives (1% of 9,999). So probability = 1/(1+100) ≈ 1%. This tests base rate neglect.
Question 9
You test positive for a rare disease (1 in 10,000 prevalence). The test is 99% accurate (1% false positive rate).
What is the approximate probability you actually have the disease?
With 10,000 people: 1 true case, but 100 false positives (1% of 9,999). So probability = 1/(1+100) ≈ 1%. This tests base rate neglect.
Question 10
You test positive for a rare disease (1 in 10,000 prevalence). The test is 99% accurate (1% false positive rate).
What is the approximate probability you actually have the disease?
With 10,000 people: 1 true case, but 100 false positives (1% of 9,999). So probability = 1/(1+100) ≈ 1%. This tests base rate neglect.
Question 11
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
Question 12
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
Question 13
You test positive for a rare disease (1 in 10,000 prevalence). The test is 99% accurate (1% false positive rate).
What is the approximate probability you actually have the disease?
With 10,000 people: 1 true case, but 100 false positives (1% of 9,999). So probability = 1/(1+100) ≈ 1%. This tests base rate neglect.
Question 14
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
Question 15
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
Question 16
You test positive for a rare disease (1 in 10,000 prevalence). The test is 99% accurate (1% false positive rate).
What is the approximate probability you actually have the disease?
With 10,000 people: 1 true case, but 100 false positives (1% of 9,999). So probability = 1/(1+100) ≈ 1%. This tests base rate neglect.
Question 17
You test positive for a rare disease (1 in 10,000 prevalence). The test is 99% accurate (1% false positive rate).
What is the approximate probability you actually have the disease?
With 10,000 people: 1 true case, but 100 false positives (1% of 9,999). So probability = 1/(1+100) ≈ 1%. This tests base rate neglect.
Question 18
You test positive for a rare disease (1 in 10,000 prevalence). The test is 99% accurate (1% false positive rate).
What is the approximate probability you actually have the disease?
With 10,000 people: 1 true case, but 100 false positives (1% of 9,999). So probability = 1/(1+100) ≈ 1%. This tests base rate neglect.
Question 19
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
Question 20
You test positive for a rare disease (1 in 10,000 prevalence). The test is 99% accurate (1% false positive rate).
What is the approximate probability you actually have the disease?
With 10,000 people: 1 true case, but 100 false positives (1% of 9,999). So probability = 1/(1+100) ≈ 1%. This tests base rate neglect.
📝 Continue your Statistical Reasoning practice. Worksheet 4 focuses on common variations practice.