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: Worksheet 6 - Intermediate-Advanced Practice Statistical Reasoning INTERMEDIATE ADVANCED
Ready to master Statistical Reasoning? This timed practice ⚡ worksheet (6/10) presents 20 intermediate-advanced-level challenges. Focus area: speed building. Learn to solve statistical reasoning tricks, handle statistical reasoning shortcut methods, and perfect statistical reasoning bank exam questions with our step-by-step solutions.
What you'll learn in this worksheet:
- Understand the logic behind statistical reasoning shortcut methods
- Learn step-by-step approaches to speed building
- Handle multi-step problems systematically
- Master complex problem variations and edge cases
- Master statistical reasoning tricks through focused practice
Your progress through Statistical Reasoning
Worksheet 6 of 10 (55% 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
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
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
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 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
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
Question 8
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
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
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 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
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 20
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
🏆 Halfway champion! 55% through Statistical Reasoning. Keep the momentum!