A failure to reject a null hypothesis that is actually false.
Readers of statistical hypothesis testing and research methods read this in relation to concepts like Type I Error and test power.
01what it is and how it works
A Type II Error is defined as the failure to reject a null hypothesis that is actually false. In a hypothesis test the null hypothesis represents the status quo or a default position. When the null is false but the test does not detect this, the probability of making this mistake is called beta (β). The complement, one minus beta, is the test's power, the likelihood of correctly identifying a true effect. This error is distinct from a Type I Error, which is an incorrect rejection of a true null hypothesis.
It is when a test does not find a real difference that actually exists.
02what to do about it
- Increase the sample size to provide more information
- Raise the significance level (alpha) if a higher tolerance for false positives is acceptable
- Select a more powerful statistical test or one that matches the data structure
- Reduce variability in the data through better measurement or design
- Use a one‑sided test when the direction of the effect is known
03how it is measured or noticed
Researchers detect a Type II Error by examining the test's power curve or by calculating the achieved power for the observed effect size and sample size. Software packages can compute beta given an assumed effect size and desired power. In practice, a non‑significant result may indicate either a true absence of effect or a Type II Error, so additional checks such as confidence intervals, effect‑size estimates, or replication studies are used to confirm whether the test had sufficient power.
04common mistakes
- Confusing it with a Type I Error
- Assuming a non‑significant p‑value always means no effect exists
- Ignoring the role of sample size in determining power
- Overlooking that power depends on the true effect size
- Treating all non‑rejections as errors without checking test assumptions
05limits and confusions
A Type II Error applies only to formal hypothesis tests that have a defined null and alternative hypothesis and a known sampling distribution. It does not apply to decisions made without statistical testing, such as anecdotal judgments or machine‑learning model selections that use different error metrics. The concept is often confused with false discovery rate or with the notion of "no effect" in practical terms, but those are separate notions of error.
06worked example
If a clinical trial shows no statistically significant difference between a new drug and placebo (p=0.15) but the drug actually reduces symptoms, the study has committed a Type II Error.
The entry above is written by GetLoopLoop. What follows is what independent catalogues hold about the same term — none of it is the source of this page.
- Also called
- false negative, false-negative, FN
- Part of
- type I and type II errors
- Kind of thing
- type of error
The same term on Wikipedia
Catalogued in 10 languagesFrequently asked questions
How is a Type II Error different from a Type I Error?
A Type II Error is a false negative, where a real effect is missed, while a Type I Error is a false positive, where an effect is detected when none exists. The two errors move in opposite directions: reducing one typically increases the other, so the choice depends on which mistake carries more risk in the specific context.
When should I worry about a Type II Error in my analysis?
You should worry about a Type II Error when failing to find an effect could lead to missed opportunities or incorrect conclusions, such as in medical trials or brand visibility studies. It depends on the stakes of the decision and whether the cost of missing a real change outweighs the cost of a false alarm.
How do researchers detect or measure a Type II Error?
Researchers detect a Type II Error by calculating the statistical power of a test, which is the probability of correctly rejecting a false null hypothesis. Low power, often due to small sample sizes or weak effects, signals a higher risk of missing real differences.
What are the limits of applying Type II Error to real-world data?
A Type II Error applies only to formal hypothesis tests with a defined null and alternative hypothesis and a known sampling distribution. It does not apply to exploratory analyses or situations where the underlying assumptions of the test are violated.
What happens if I make a Type II Error in my brand measurement?
If you make a Type II Error in brand measurement, you might conclude that your brand has no visibility issues when it actually does, leading to missed corrective actions. This could result in declining brand recognition or lost market share that goes unaddressed.
Asked out loud
spoken, not typedThe same term in the words somebody uses speaking to an assistant rather than typing into a box — written from the situation, which is why each one carries the situation it came from.
Yes, you likely missed a real effect. A false negative means your test lacked the sensitivity to detect a difference that actually exists, often due to low statistical power.
It depends on your sample size and effect size. If the test was underpowered, you might overlook a real drop in visibility, leading to a campaign that underperforms.
Usually, it comes down to statistical power. If the test wasn't designed to detect small but meaningful changes, it can miss real effects even when they're present.