Error Type II (Errors in Hypothesis Testing)
- engacy2
- Jul 13, 2024
- 1 min read
In hypothesis testing, two types of errors can occur when making a decision about the null hypothesis.
These errors are known as Type I and Type II errors.
Additionally, the Standard Error of Mean (SEM) is an important concept that measures the precision of the sample mean estimate.
Definition
A Type II error occurs when the null hypothesis (H0) is false, but it is incorrectly accepted (failed to reject).
It is also known as a "false negative" or "beta error."
Characteristics
Probability (β): The probability of committing a Type II error is denoted by β. Power of a test is defined as 1 – β, which represents the probability of correctly rejecting H0.
Example: If β=0.20, there is a 20% chance of failing to reject the null hypothesis when it is actually false.
Consequences
Type II errors can lead to the conclusion that no effect or difference exists when there is one, potentially overlooking important scientific findings and beneficial treatments.
Example in Pharmaceuticals
Suppose a pharmaceutical company tests a new drug and concludes it is not effective (failing to reject H0) when, in reality, it is effective.
This can result in a potentially beneficial drug being discarded.
