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 - Intermediate Level: tricky scenarios handling Statistical Reasoning INTERMEDIATE
This expert challenge 📈 worksheet focuses on Statistical Reasoning - a key topic in Strong Weak Arguments. You'll solve 20 intermediate-level problems (Worksheet 5 of 10). The primary focus is on tricky scenarios handling. Master how to solve statistical reasoning, statistical reasoning tricks, and statistical reasoning shortcut methods through systematic practice.
What you'll learn in this worksheet:
- Master how to solve statistical reasoning through focused practice
- Understand the logic behind statistical reasoning tricks
- Learn step-by-step approaches to tricky scenarios handling
- Improve your solving speed while maintaining accuracy
- Learn to eliminate wrong options efficiently
Your progress through Statistical Reasoning
Worksheet 5 of 10 (44% 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
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
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
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
What is the primary weakness in this argument?
Small, non-random sample (n=5) cannot support population-wide conclusions regardless of unanimity.
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
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
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
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
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.
📝 Continue your Statistical Reasoning practice. Worksheet 5 focuses on tricky scenarios handling.