Think Bit — Newspot Nigeria Sensitivity vs Specificity: Understanding the Difference with Simple Examples

Sponsored Advert

By Newspot Nigeria Science Desk

✅ Sensitivity Calculation

✅ Formula:

Sensitivity (S) = True Positives (TP) ÷ (True Positives + False Negatives)

✅ Example 1: Cheat Detection Test

• 10 students actually cheated.

• The test correctly catches 8 cheaters (True Positives).

• It misses 2 cheaters (False Negatives).

  Advertisement
  
  

Calculation:
Sensitivity = 8 ÷ (8 + 2)
Sensitivity = 8 ÷ 10
Sensitivity = 0.8 (80%)

✅ Example 2: COVID-19 Test

• 200 people are tested.

• 50 people have COVID-19.

• The test correctly identifies 45 as positive (True Positives).

• It misses 5 positive cases (False Negatives).

Calculation:
Sensitivity = 45 ÷ (45 + 5)
Sensitivity = 45 ÷ 50
Sensitivity = 0.9 (90%)

Sponsored

✅ Example 3: Pregnancy Test

• 1,000 women take a pregnancy test.

• 100 women are actually pregnant.

• The test correctly identifies 95 pregnant women (True Positives).

• It misses 5 pregnant women (False Negatives).

Calculation:
Sensitivity = 95 ÷ (95 + 5)
Sensitivity = 95 ÷ 100
Sensitivity = 0.95 (95%)

Sponsored

Sponsored

✅ Specificity Calculation

✅ Formula:

Specificity (S) = True Negatives (TN) ÷ (True Negatives + False Positives)

✅ Example 1: Cheat Detection Test

• 90 students did not cheat.

• The test correctly identifies 85 honest students (True Negatives).

• It wrongly accuses 5 honest students (False Positives).

Calculation:
Specificity = 85 ÷ (85 + 5)
Specificity = 85 ÷ 90
Specificity = 0.94 (94%)

✅ Example 2: COVID-19 Test

• 150 people do not have COVID-19.

• The test correctly identifies 140 as negative (True Negatives).

• It wrongly identifies 10 as positive (False Positives).

Calculation:
Specificity = 140 ÷ (140 + 10)
Specificity = 140 ÷ 150
Specificity = 0.93 (93%)

✅ Example 3: Cancer Screening Test

• 5,000 people without cancer are screened.

• The test correctly identifies 4,950 as negative (True Negatives).

• It wrongly shows 50 as positive (False Positives).

Calculation:
Specificity = 4,950 ÷ (4,950 + 50)
Specificity = 4,950 ÷ 5,000
Specificity = 0.99 (99%)

✅ Why This Matters

• ✅ High Sensitivity: The test is good at detecting those with the condition (few missed cases).

• ✅ High Specificity: The test is good at avoiding false positives (protects those without the condition).

✅ Quick Summary:

• Sensitivity: Measures how well the test identifies true positives.

• Specificity: Measures how well the test identifies true negatives.

• ✅ Sensitivity Formula:
  Sensitivity = True Positives ÷ (True Positives + False Negatives)

• ✅ Specificity Formula:
  Specificity = True Negatives ÷ (True Negatives + False Positives)

Sponsored