Type I or Type II?

Read real-world hypothesis test scenarios, examine the reality and the decision made, then identify the type of error—or recognize when no error occurred.

How to Play

  • Each round presents a hypothesis test with a null hypothesis (H₀)
  • You’ll see what’s actually true and what decision was made
  • Type I Error — Rejecting H₀ when it is actually true (false positive)
  • Type II Error — Failing to reject H₀ when it is actually false (false negative)
  • No Error — The decision matches reality (correct decision)
  • 10 rounds · +10 points per correct answer · 100 points possible
  • Grading: A (90+), B (80+), C (70+), D (60+), F (<60)

Game Over

You scored out of 100

Quick Reference

Type I Error (α)
Rejecting H₀ when H₀ is actually true. A “false alarm”—you concluded there was an effect when there really wasn’t one.
Type II Error (β)
Failing to reject H₀ when H₀ is actually false. A “miss”—you failed to detect a real effect that was actually there.
No Error
The statistical decision matches reality. Either you correctly rejected a false H₀ or correctly failed to reject a true H₀.
Remember: Type I = false positive · Type II = false negative