Free lesson · Biostatistics
Free · No sign-upScreening test math, finally clear
Free for everyone, with no account. Covers: Building and reading a 2×2 table · Sensitivity, specificity, PPV, and NPV · How prevalence changes predictive values · Cutoffs, ROC curves, and likelihood ratios · Type I and type II error, power, and confidence intervals.
Screening test math with the 2×2 table
Sensitivity, specificity, predictive values, prevalence, cutoffs, and likelihood ratios: the screening-test math that shows up on every board exam. Watch the video (it pauses to quiz you), review the flashcards, then run the drill.
The 2×2 table, start to finish
How to draw the 2×2 table, read sensitivity and specificity down the columns and predictive values across the rows, and see why prevalence changes PPV and NPV.
Preview lesson with automated narration. A version recorded by Dr. Dave will replace it.
Lesson flashcards
18 cards
Practice questions
12 questions
A new rapid test for strep pharyngitis is studied in 500 children. 100 have strep on throat culture; the rapid test is positive in 85 of them. Of the 400 without strep, the rapid test is negative in 360.
Question 1 of 12
What is the sensitivity of the rapid test, as a percentage?
Make it stick
Biostatistics mnemonics
Each one is also in the flashcard deck above (tagged Mnemonic), so spaced repetition brings it back right before you would forget it.
SnNout
A highly Sensitive test, when Negative, rules OUT disease. Screen with it.
SpPin
A highly Specific test, when Positive, rules IN disease. Confirm with it.
PPV and Prevalence are Pals
PPV rises and falls with prevalence; NPV moves the opposite way.
Disease across the top, test down the side
Draw the 2×2 the same way every time: a = TP, b = FP, c = FN, d = TN.
Type I: "I see it" · Type II: "II blind"
Type I = false positive (α). Type II = false negative, missing a real effect (β).
Quick reference
Formula sheet
| Measure | Formula |
|---|---|
| Sensitivity | a / (a + c) |
| Specificity | d / (b + d) |
| Positive predictive value | a / (a + b) |
| Negative predictive value | d / (c + d) |
| Positive likelihood ratio | sensitivity / (1 − specificity) |
| Negative likelihood ratio | (1 − sensitivity) / specificity |
| Power | 1 − β |
Keep going
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