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

   β error

2. B

   α error

3. C

   1α

4. D

   2β

Appeared in: NORCET 5 mains

Explanation

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• The scenario describes a Type II error, which is also denoted by the Greek letter β (beta).
• A Type II error occurs when a study concludes there is no effect or no difference between groups, when in reality, an effect or difference does exist.
• In this case, the study failed to detect a true difference between the two antidiabetic drugs, leading to a 'false negative' conclusion.
• This happens when a false null hypothesis (the assumption of 'no difference') is not rejected by the study's findings.

Why Other Options Were Wrong

• Option B: An α (alpha) error, or Type I error, is the opposite. It occurs when a study incorrectly concludes there IS a difference, when in reality, there is none (a 'false positive').
• Option C: '1α' is not a standard term in statistics for describing hypothesis testing errors.
• Option D: '2β' is not a standard statistical term. The probability of a Type II error is denoted simply by β.

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Clinical Relevance

• Nursing practice connection: This is primarily an exam-oriented knowledge point with limited direct bedside application, so retain Type I and Type II errors in statistical hypothesis testing as background academic context rather than a clinical decision trigger.
• Nurses must be able to critically appraise research. Understanding Type II errors is vital because it means a potentially beneficial new drug or intervention could be wrongly dismissed as ineffective.
• A study's ability to avoid a Type II error is called its 'statistical power'. Low power, often due to a small sample size, increases the risk of missing a true effect.
• What if the sample size of the trial was very small? A small sample size reduces the statistical power of a study, making it much more likely to commit a Type II error and miss a real difference between the drugs.

How to Approach the Question

• First, identify the two key pieces of information in the stem: the study's conclusion and the actual reality.
• Study's Conclusion: 'no significant difference was found'. This means the null hypothesis (H0: no difference) was NOT rejected.
• The Reality: 'there was a significant difference'. This means the null hypothesis was actually false.
• Recognize that failing to reject a false null hypothesis is the definition of a Type II error.
• Match this definition to the options provided. A Type II error is also known as a β error.

Concept Tested & Keywords

• Concept Tested: Type I and Type II errors in statistical hypothesis testing.
• 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

QXVnq1qUK1vpvOioWBQnxo

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. 2911-2913, 2912-2914