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Master Statistical Reasoning - Intermediate-Advanced Level Problems Statistical Reasoning INTERMEDIATE ADVANCED

Excel in competitive exams with this self assessment worksheet on Statistical Reasoning. Worksheet 7 of 10 contains 20 intermediate-advanced-level problems. Target your accuracy improvement skills while practicing statistical reasoning shortcut methods, statistical reasoning bank exam questions, and statistical reasoning ssc cgl.

📝 Worksheet 7 of 10 • 20 questions • ⏱️ Estimated time: 20 minutes • 🎯 Intermediate Advanced level

What you'll learn in this worksheet:
Your progress through Statistical Reasoning
Worksheet 7 of 10 (66% 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

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

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

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

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

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

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

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

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