Which type of error is committed in testing a hypothesis when the researcher accepts the null hypothesis that is actually false?

Appeared in: JODHPUR AIIMS SNO-2018

Explanation

A Type II error is committed when a researcher accepts a null hypothesis that is, in reality, false.
This type of error is also referred to as a 'false negative' or a beta (β) error.
Essentially, the study fails to detect an effect, association, or difference that truly exists in the population.
An example would be concluding a new drug has no effect on blood pressure, when it actually does have a small but real effect that the study was not sensitive enough to detect.

Why Other Options Were Wrong

Option A: A Type I error occurs when a researcher rejects a null hypothesis that is actually true. The question describes accepting a false null hypothesis.
Option B: Type III error is not a standard error in basic hypothesis testing. It typically refers to correctly rejecting the null hypothesis but for the wrong reason or concluding the wrong direction of an effect.
Option D: Type IV error is also not a standard error type. It sometimes refers to the misinterpretation of a correctly rejected hypothesis (e.g., a 'post-hoc' error).

Related Visual

Clinical Relevance

Nursing practice connection: This is primarily an exam-oriented knowledge point with limited direct bedside application, so retain Hypothesis Testing Errors (Type I vs. Type II) as background academic context rather than a clinical decision trigger.
In clinical research, a Type II error can have serious consequences. For example, a study might conclude that a new, effective cancer drug is no better than a placebo.
This false negative result could prevent a beneficial treatment from being approved and reaching patients who need it.
What if the study had a larger sample size? Increasing the sample size increases the statistical power of a study, which reduces the probability of committing a Type II error. A larger study is more likely to detect a true effect, even if it is small.

How to Approach the Question

First, break down the question into two parts: the researcher's action and the reality of the situation.
Researcher's action: 'accepts the null hypothesis'.
Reality: 'the null hypothesis... is actually false'.
Next, recall the definitions of the primary types of statistical errors.
A Type I error is rejecting a TRUE null hypothesis.
A Type II error is accepting a FALSE null hypothesis.

Concept Tested & Keywords

Concept Tested: Hypothesis Testing Errors (Type I vs. Type II)
Stem keywords: hypothesis testing, error, accepts, null hypothesis, false
Lead-in keywords: Which type
Negative lead-in flag: false

Question ID

QPhbdaWa2j9N21gJzeix2z

Reference Book

E6 NursingResearch Suresh Sharma 5e Part 2 p. 188-190

E6 NursingResearch Polit 11e Part 1 p. 404-406

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

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