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Statistical Reasoning: Worksheet 2 - Beginner Practice Statistical Reasoning BEGINNER

Ready to master Statistical Reasoning? This entry level practice worksheet (2/10) presents 20 beginner-level challenges. Focus area: pattern recognition. Learn to solve statistical reasoning reasoning questions, handle statistical reasoning practice, and perfect statistical reasoning for competitive exams with our step-by-step solutions.

📝 Worksheet 2 of 10 • 20 questions • ⏱️ Estimated time: 20 minutes • 🎯 Beginner level

What you'll learn in this worksheet:
Your progress through Statistical Reasoning
Worksheet 2 of 10 (11% complete)

Question 1

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 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

What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.

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

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

What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.

Question 14

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 15

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 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

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

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.
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