**Core Concept**
This question tests understanding of statistical error types in hypothesis testing, specifically in the context of rejecting a null hypothesis when it is actually true. The null hypothesis (H₀) represents the absence of a significant effect or difference.

**Why the Correct Answer is Right**
When the null hypothesis is rejected even though it is true, this is defined as a Type I error. It occurs at the significance level (α), typically 0.05, and is also known as a false positive. The probability of this error is denoted by α (alpha). This concept is fundamental in clinical and dental research, where false positives may lead to inappropriate treatment recommendations.

**Why Each Wrong Option is Incorrect**
Option B: Type II error (beta error) occurs when the null hypothesis is *not rejected* when it is actually false — the opposite of the scenario described.
Option C: "Gamma error" is not a recognized term in standard statistical hypothesis testing and is not used in medical or dental research.
Option D: Since only A is correct, "all of the above" is incorrect.

**Clinical Pearl / High-Yield Fact**
In dental research, a Type I error (alpha error) can lead to over-treatment or unnecessary interventions — always ensure the significance level is carefully chosen to avoid false positives.

✓ Correct Answer: A. Type I alpha error