Definition
A Type II Error (also known as a “False Negative”) is the error of accepting a null hypothesis that is actually false. In modeling and simulation, it is the Model User’s Risk: accepting and using a model that is actually invalid.
- How to read: “Type two error.”
- Meaning: Failing to detect a real effect or a real flaw.
Why It Matters
Type II errors are often the most dangerous in high-stakes environments. If an invalid simulator is accepted for pilot training, the resulting lack of skill or incorrect muscle memory can lead to real-world crashes. It is the risk of misplaced trust.
Core Concepts
- False Negative: Concluding that no effect exists when one actually does, or that a system is safe when it is flawed.
- Model User’s Risk: The risk that the person relying on the model will suffer consequences because the model’s flaws were not detected.
- Beta (): The probability of making a Type II error. The “Power” of a test is defined as .