If we reject null hypothesis, when actually it is true it is known as –
First, the core concept here is statistical hypothesis testing. The null hypothesis is the default assumption that there's no effect or no difference. When we conduct a test, we either reject or fail to reject the null based on the data. The question is about a Type I error, which occurs when we incorrectly reject a true null hypothesis. That's a fundamental concept in statistics, so the core concept should explain that.
Now, why is the correct answer right? The Type I error is also known as a false positive. It's the probability of this error that's denoted by alpha (α), typically set at 0.05. The explanation should mention that rejecting the null when it's true leads to a Type I error, and the significance level is the threshold for this error.
For the incorrect options, let's consider common distractors. Type II error is when we fail to reject a false null hypothesis (false negative). Beta (β) is related to Type II error. Then there's the p-value, which is the probability of observing the data if the null is true, not the error itself. Power is 1 - β, which is the probability of correctly rejecting a false null. So each wrong option needs a brief explanation of why they don't fit the question.
The clinical pearl here is to remember that Type I is "I for incorrect rejection" and Type II is "II for incorrect acceptance." Maybe a mnemonic like "Alpha is for Type I, Beta for Type II" could help. Also, emphasizing that Type I is more about falsely claiming an effect exists when it doesn't.
Finally, the correct answer is Type I error. Need to make sure all sections are covered concisely without exceeding the character limit. Check for clarity and that each section is properly addressed.
**Core Concept**
This question tests understanding of statistical hypothesis testing, specifically **Type I and Type II errors**. Rejecting a true null hypothesis represents a **Type I error**, whereas failing to reject a false null hypothesis represents a **Type II error**. These errors underpin statistical decision-making in clinical research and diagnostics.
**Why the Correct Answer is Right**
A **Type I error** occurs when a researcher incorrectly rejects a **true null hypothesis** (i.e., falsely concludes an effect exists when it does not). This is also termed a "false positive" and is quantified by the **significance level (α)**, typically set at 0.05. For example, if a drug has no real effect but a study concludes it does, this is a Type I error. The mechanism involves statistical sampling variability leading to an erroneous rejection of the null hypothesis.
**Why Each Wrong Option is Incorrect**
**Option A:** *Type II error* involves failing to reject a **false null hypothesis** (false negative), not rejecting a true one.
**Option B:** *Power* is the probability of correctly rejecting a false null hypothesis (1 - β), unrelated to rejecting a true null.
**Option C:** *P-value* is the probability of observing the data (or more extreme) if the null hypothesis is true; it does not define the error