Final answer:
The statement is false. A Type I Error occurs when a researcher incorrectly rejects a true null hypothesis, causing a false positive. A Type II Error, on the other hand, is not rejecting a false null hypothesis, resulting in a false negative.
Step-by-step explanation:
The question you've asked about relates to statistical hypothesis testing. Let's clarify the concept of a Type I Error. The statement 'A Type I Error refers to the researcher's conclusion that no difference exists when in fact a difference does exist' is false. A Type I Error actually occurs when a researcher incorrectly rejects a true null hypothesis, concluding there is a statistically significant difference when there is none. It's a false positive error. For example, saying that a medication has an effect when it really does not would be a Type I error. The magnitude of this error is denoted by the Greek letter alpha (α).
Conversely, a Type II Error is the error that occurs when a researcher fails to reject a false null hypothesis, suggesting there is no significant difference when, in fact, there is one. It's a false negative error. The probability of committing a Type II Error is denoted by the Greek letter beta (β).