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 10 - Expert Practice Statistical Reasoning EXPERT
Ready to master Statistical Reasoning? This accuracy focus 👑 worksheet (10/10) presents 20 expert-level challenges. Focus area: application-based learning. Learn to solve statistical reasoning reasoning tricks, handle fast statistical reasoning solving, and perfect statistical reasoning mastery with our step-by-step solutions.
What you'll learn in this worksheet:
- Understand the logic behind fast statistical reasoning solving
- Learn step-by-step approaches to application-based learning
- Achieve mastery with the most difficult problem types
- Perfect your speed and accuracy under time pressure
- Master statistical reasoning reasoning tricks through focused practice
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Worksheet 10 of 10 (100% 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
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
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
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
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
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
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
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
🏆 Congratulations! You've mastered Statistical Reasoning - all 10 worksheets completed!