Statistics for Everyday Life
Type I vs Type II Error
In any test or decision under uncertainty, two opposite mistakes are possible. Statisticians call them Type I and Type II errors — the false positive and the false negative.
| Aspect | Type I error | Type II error |
|---|---|---|
| Also known as | False positive | False negative |
| What happens | Detecting an effect that isn’t real | Missing an effect that is real |
| In a court analogy | Convicting an innocent person | Acquitting a guilty person |
| Medical test analogy | Healthy person flagged as sick | Sick person told they’re healthy |
| Controlled by | The significance threshold (e.g., 5%) | Sample size and effect size (statistical power) |
When to use type i error
Worry most about Type I errors when a false alarm is costly — approving a useless drug, or convicting the innocent — and set a stricter threshold.
When to use type ii error
Worry most about Type II errors when missing a real effect is costly — failing to catch a disease, or overlooking a real safety risk — and use a larger sample for more power.
Frequently asked questions
- How do I remember which is which?
- Type I = false positive (you saw something that wasn’t there). Type II = false negative (you missed something that was). One memory aid: "Type I, you cry wolf; Type II, you miss the wolf."
- Can you avoid both at once?
- There’s a trade-off: making a test stricter to reduce false positives tends to increase false negatives, and vice versa. The main way to reduce both together is to gather more data (a larger sample), which increases statistical power.
- What is statistical power?
- Power is the probability of correctly detecting a real effect — that is, of avoiding a Type II error. It rises with larger samples, bigger true effects, and less noisy data.