Question:
In respect of type-I error in the field of medical statistics, which one of the following is NOT correct?
In respect of type-I error in the field of medical statistics, which one of the following is NOT correct?
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Remember that 1 minus beta is the definition of statistical power, not of alpha.
Updated On: Jul 7, 2026
- It is also called alpha error.
- It is often assigned a value of 0.05 in studies.
- It is equal to 1 minus the beta error.
- It is used to determine sample size.
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The Correct Option is C
Solution and Explanation
Step 1: Understanding the Question.
A type-I error happens when a study wrongly rejects a true null hypothesis, in other words a false positive result. We need to find the one statement about it that is wrong.
Step 2: Key Formula or Approach.
Type-I error is written as alpha. Type-II error is written as beta, and the quantity 1 minus beta is called the power of the study, the chance of correctly detecting a true difference. These are two separate ideas that should not be mixed up.
Step 3: Detailed Explanation.
Statement (a) is correct, alpha error is the standard name for type-I error. Statement (b) is correct, researchers commonly fix alpha at 0.05, meaning a 5 percent chance of a false positive is accepted. Statement (d) is correct, both alpha and beta values are needed inputs when calculating the sample size for a study. Statement (c) says type-I error equals 1 minus the beta error, but 1 minus beta is the definition of power, not of alpha. Alpha and beta are chosen somewhat independently by the researcher and are not defined in terms of each other this way, so statement (c) is the false one.
Step 4: Final Answer.
The incorrect statement is that type-I error equals 1 minus the beta error.
\[ \boxed{\text{Statement (c) is NOT correct}} \]
A type-I error happens when a study wrongly rejects a true null hypothesis, in other words a false positive result. We need to find the one statement about it that is wrong.
Step 2: Key Formula or Approach.
Type-I error is written as alpha. Type-II error is written as beta, and the quantity 1 minus beta is called the power of the study, the chance of correctly detecting a true difference. These are two separate ideas that should not be mixed up.
Step 3: Detailed Explanation.
Statement (a) is correct, alpha error is the standard name for type-I error. Statement (b) is correct, researchers commonly fix alpha at 0.05, meaning a 5 percent chance of a false positive is accepted. Statement (d) is correct, both alpha and beta values are needed inputs when calculating the sample size for a study. Statement (c) says type-I error equals 1 minus the beta error, but 1 minus beta is the definition of power, not of alpha. Alpha and beta are chosen somewhat independently by the researcher and are not defined in terms of each other this way, so statement (c) is the false one.
Step 4: Final Answer.
The incorrect statement is that type-I error equals 1 minus the beta error.
\[ \boxed{\text{Statement (c) is NOT correct}} \]
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