A RCT comparing the efficacy of 2 drugs showed a difference between them (with a p-value < 0.05). Assume that in reality, however, the two drugs don't differ. This is?

1. A

   Type 1 Error

2. B

   Type 2 Error

3. C

   Type 3 Error

4. D

   Type 4 Error

Appeared in: AIIMS CRE SNO - 2023

Explanation

• A Type 1 error occurs when a researcher incorrectly rejects a true null hypothesis.
• This is also known as a 'false positive' or an alpha (α) error.
• In the given scenario, the study found a significant difference (p < 0.05), leading to the rejection of the null hypothesis (the assumption of no difference).
• However, the problem states that in reality, the drugs do not differ, meaning the null hypothesis was true.
• Therefore, rejecting a true null hypothesis is the definition of a Type 1 error.

Why Other Options Were Wrong

• Option B: A Type 2 error is a 'false negative,' where a study fails to detect a real difference. The scenario describes finding a difference, not missing one.
• Option C: Type 3 error is not a standard statistical term and refers to a different kind of mistake (e.g., getting the direction of the effect wrong). It does not fit the scenario of finding a difference that doesn't exist.
• Option D: Type 4 error is not a standard, universally accepted term in biostatistics and is not relevant to this common hypothesis testing scenario.

Related Visual

• Visual 1: 2x2 Table. A grid illustrating the four possible outcomes of hypothesis testing: True Positive (Correct), True Negative (Correct), Type 1 Error (False Positive), and Type 2 Error (False Negative). This visually clarifies the relationship between study results and reality.

Clinical Relevance

• Nursing practice connection: This is primarily an exam-oriented knowledge point with limited direct bedside application, so retain Statistical Errors in Hypothesis Testing as background academic context rather than a clinical decision trigger.
• Understanding statistical errors is crucial for evidence-based nursing practice. A Type 1 error can lead to the adoption of an ineffective or even harmful treatment, believing it to be superior.
• This can result in negative patient outcomes, increased healthcare costs, and misdirection of future research efforts.
• What if? If the p-value was 0.08 (not significant) but in reality, one drug was truly superior, this would be a Type 2 error. A potentially valuable treatment might be wrongly discarded, denying patients a beneficial therapy.

How to Approach the Question

• First, identify the null hypothesis (H₀), which is the statement of no effect or no difference. Here, H₀ is 'The two drugs do not differ in efficacy.'
• Next, determine the study's conclusion based on the p-value. A p-value < 0.05 means the result is statistically significant, so the study rejects the null hypothesis.
• Then, identify the 'reality' as stated in the question. Here, 'in reality... the two drugs don't differ,' which means the null hypothesis is actually true.
• Finally, match the situation to the correct definition. The study rejected a null hypothesis that was, in reality, true. This is the textbook definition of a Type 1 error.

Concept Tested & Keywords

• Concept Tested: Statistical Errors in Hypothesis Testing
• Stem keywords: RCT, efficacy, p-value, Type 1 Error, Type 2 Error
• Lead-in keywords: This is
• Clinical cues: p-value < 0.05 indicates statistical significance
• Clinical cues: Study finding contradicts reality

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qkjgQBHn_yd8O9-WdtDgQ

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