In a randomized trial comparing the efficacy of two antidiabetic drugs, no significant difference was found between the two drugs. However, in reality, there was a significant difference in the two drugs. This is an example of?

1. A

   Beta error

2. B

   Alpha error

3. C

   1-Alpha error

4. D

   1-Beta error

Appeared in: NORCET -4 , 2023

Explanation

• A Beta error, also known as a Type II error, occurs when a study fails to detect a difference or effect that is actually present.
• In this scenario, the study's conclusion was 'no significant difference,' which means it failed to reject the null hypothesis (the hypothesis of no difference).
• However, in reality, a 'significant difference' did exist, meaning the null hypothesis was false.
• Therefore, the study made a Type II or Beta error by incorrectly accepting a false null hypothesis, resulting in a 'false negative' conclusion.

Why Other Options Were Wrong

• Option B: An Alpha error (Type I error) is the opposite situation. It occurs when a study concludes there IS a significant difference, but in reality, there is NOT. This is a 'false positive'.
• Option C: 1-Alpha is the confidence level of a study, not an error. It represents the probability of correctly concluding there is no difference when no difference exists.
• Option D: 1-Beta is the statistical power of a study, which is the probability of correctly detecting a difference when one truly exists. It is the direct opposite of a Beta error.

Related Visual

Clinical Relevance

• Nursing practice connection: This is primarily an exam-oriented knowledge point with limited direct bedside application, so retain Types of errors in hypothesis testing (Type I and Type II errors) as background academic context rather than a clinical decision trigger.
• Nurses must be able to critically appraise research to ensure evidence-based practice. Understanding statistical errors is fundamental to this skill.
• A Type II (Beta) error can lead to the premature abandonment of a potentially beneficial treatment or intervention because the study wasn't powerful enough to detect its effect.
• A Type I (Alpha) error can lead to the adoption of a new, ineffective, and possibly more expensive or harmful treatment, based on a false positive result.

How to Approach the Question

• First, identify the two conflicting pieces of information: the study's finding and the 'reality'.
• Study Finding: 'no significant difference'. In statistical terms, this means the study failed to reject the null hypothesis (H0: no difference).
• Reality: 'there was a significant difference'. This means the null hypothesis was actually false.
• Recall the definitions of statistical errors. Failing to reject a null hypothesis when it is false is the definition of a Type II error.
• Match the term 'Type II error' with its synonym, 'Beta error', to select the correct option.

Concept Tested & Keywords

• Concept Tested: Types of errors in hypothesis testing (Type I and Type II errors)
• Stem keywords: randomized trial, antidiabetic drugs, no significant difference, reality, significant difference
• Lead-in keywords: This is an example of
• Negative lead-in flag: false

Question ID

Q1vSbq8MI7JtYZfxc5qeP5

Copy ID

Reference Book

E6 NursingResearch Sukhpal Kaur p. 484-486

E6 Robert Boland, Marcia L. Verduin - Kaplan and Sadock's Comprehensive Text of Psychiatry-Wolters Kluwer Health (2024) (pp 1-16525 of 16525) pp. 2912-2914, 2911-2913