**Core Concept**
In statistics, hypothesis testing is used to assess the validity of a hypothesis. A null hypothesis states that there is no significant difference or relationship between variables, while an alternative hypothesis suggests a significant difference or relationship. Type I and Type II errors occur when the null hypothesis is rejected or accepted, respectively, when it is actually true or false.

**Why the Correct Answer is Right**
Type I error occurs when the null hypothesis is rejected, even though it is true. This is often due to chance or sampling variability. In the context of hypothesis testing, Type I error is also known as alpha error or the "error of the first kind." It is denoted by the Greek letter alpha (α). The probability of Type I error is typically set at 0.05, meaning that there is a 5% chance of rejecting the null hypothesis when it is actually true.

**Why Each Wrong Option is Incorrect**
**Option A:** Type II error is the incorrect rejection of a false null hypothesis, not the false rejection of a true null hypothesis. Type II error is also known as beta error.
**Option C:** Beta error is another term for Type II error, which is the incorrect failure to reject a false null hypothesis.
**Option D:** Error of the second kind is another term for Type II error, which is the incorrect failure to reject a false null hypothesis.

**Clinical Pearl / High-Yield Fact**
In hypothesis testing, the probability of Type I error (α) and the probability of Type II error (β) are inversely related. As α increases, β decreases, and vice versa. This means that reducing the risk of Type I error may increase the risk of Type II error, and vice versa.

**✓ Correct Answer: B. Type I error**