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 - Expert Level: conceptual clarity Statistical Reasoning EXPERT
This skill evaluation ⚡ worksheet focuses on Statistical Reasoning - a key topic in Strong Weak Arguments. You'll solve 20 expert-level problems (Worksheet 9 of 10). The primary focus is on conceptual clarity. Master statistical reasoning ssc cgl, statistical reasoning reasoning tricks, and fast statistical reasoning solving through systematic practice.
What you'll learn in this worksheet:
- Master statistical reasoning ssc cgl through focused practice
- Understand the logic behind statistical reasoning reasoning tricks
- Learn step-by-step approaches to conceptual clarity
- Develop intuition for instant problem recognition
- Achieve mastery with the most difficult problem types
Your progress through Statistical Reasoning
Worksheet 9 of 10 (88% complete)
Question 2
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 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
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
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
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
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
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
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
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 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
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
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.
🏅 Almost there! 88% through Statistical Reasoning. Only 1 worksheets left!