A Type I error is defined as?

Appeared in: NORCET 2 -2021 (Shift-2)

Explanation

A Type I error is the incorrect rejection of a true null hypothesis.
This type of error is also referred to as a 'false positive' because the researcher concludes that an effect or relationship exists when, in reality, it does not.
The probability of committing a Type I error is denoted by the Greek letter alpha (α) and is also known as the significance level of the test.
In clinical research, this could mean concluding a new treatment is effective when it is not.

Why Other Options Were Wrong

Option B: This describes a Type II error, not a Type I error. A Type II error is a 'false negative' – failing to detect an effect that is actually present.
Option C: This describes a correct decision, not an error. When the null hypothesis is false and the researcher rejects it, they have correctly identified a true effect.
Option D: This also describes a correct decision. When the null hypothesis is true and the researcher fails to reject it, they have correctly concluded that there is no evidence of an effect.

Related Visual

A 2x2 grid illustrating the four outcomes of hypothesis testing: Type I Error False Positive, Type II Error False Negative, and the two types of correct decisions. Each quad...

Clinical Relevance

Nursing practice connection: This is primarily an exam-oriented knowledge point with limited direct bedside application, so retain Definition of Type I error in hypothesis testing as background academic context rather than a clinical decision trigger.
Understanding Type I and Type II errors is crucial for interpreting nursing and medical research.
A Type I error in a clinical trial could lead to the adoption of a new, ineffective, or even harmful treatment, putting patients at risk.
A Type II error could result in a genuinely effective treatment being discarded, denying patients a beneficial therapy.

How to Approach the Question

First, identify the key term in the question: 'Type I error'. This is a factual recall question about a statistical definition.
Recall or look up the definition of the null hypothesis (H0), which is a statement of 'no effect' or 'no difference'.
Remember the two main types of errors in hypothesis testing. A helpful mnemonic is that a Type I error is a 'false positive' and a Type II error is a 'false negative'.
Evaluate each option against the definition. A Type I error means you 'find' something that isn't there. This corresponds to rejecting the null hypothesis (saying there is an effect) when it is actually true (there is no effect).
Option A directly matches this definition. The other options describe a Type II error (Option B) or correct decisions (Options C and D).

Concept Tested & Keywords

Concept Tested: Definition of Type I error in hypothesis testing
Stem keywords: Type I error, defined as
Lead-in keywords: BEST, MOST RELEVANT CLUE
Negative lead-in flag: false

Question ID

Q2xTJqRes8JVnzBgFR5zzr

Reference Book

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

E6 NursingResearch Polit 11e Part 1 pp. 403-405, 404-406

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