Final answer:
A Type I error, also known as a false positive error, occurs when a hypothesis test incorrectly rejects a true null hypothesis. This is different from a Type II error, which arises when the test fails to reject a false null hypothesis, known as a false negative error. Understanding these errors is crucial for accurate interpretation of statistical results.
Step-by-step explanation:
A Type I error in hypothesis testing is when a true null hypothesis is incorrectly rejected. This is referred to as a false positive error. When the null hypothesis is in fact true, but the test results suggest that there is a significant effect and the null hypothesis is rejected, we have committed a Type I error. The probability of a Type I error occurring is denoted by the Greek letter alpha (α). It's important to understand that Type I errors can have serious consequences depending on the context; for example, in medical testing, a Type I error could lead to a person being falsely diagnosed with a disease they don't have.
On the other hand, Type II error is the opposite; it occurs when a false null hypothesis is not rejected, which is known as a false negative error. Not rejecting a false null hypothesis means failing to detect an effect or difference when one actually exists, which is denoted by the Greek letter beta (β).
In summary, the key differences between these two types of errors are what they falsely indicate: a Type I error indicates a difference when there isn't one, and a Type II error fails to indicate a difference when there is one.