AIIMS Raipur lecturer 2021
Nursing Research & Statistic
Easy
Which among the following is a non-parametric test?
Appeared in: AIIMS Raipur lecturer 2021
Explanation
- McNemar's test is classified as a non-parametric statistical test.
- It is specifically used for analyzing paired or matched nominal data, often presented in a 2x2 contingency table.
- Unlike parametric tests, it does not assume that the data follows a particular distribution, such as a normal distribution.
- It is commonly applied in 'before-and-after' or pre-test/post-test study designs to detect changes in proportions for a dichotomous variable (e.g., presence or absence of a symptom).
Why Other Options Were Wrong
- Option A: ANOVA (Analysis of Variance) is a parametric test, not a non-parametric one.
- Option C: The t-test is a classic example of a parametric test.
- Option D: The Z-test is a parametric test used for large samples or when population variance is known.
Related Visual
Clinical Relevance
- Nursing practice connection: This is primarily an exam-oriented knowledge point with limited direct bedside application, so retain Difference between parametric and non-parametric statistical tests as background academic context rather than a clinical decision trigger.
- In nursing research, selecting the appropriate statistical test is crucial for ensuring the validity and reliability of study findings. Using a parametric test on data that doesn't meet its assumptions can lead to erroneous conclusions.
- A nurse researcher might use a McNemar test to evaluate the effectiveness of a patient education program. For instance, they could compare the proportion of patients who correctly perform a self-care task (a 'yes/no' outcome) before and after the educational intervention.
- What if? If the researcher were comparing a continuous variable (e.g., mean blood pressure) in the same group of patients before and after an intervention, and the data were normally distributed, the correct test would be a paired t-test (a parametric test), not the McNemar test.
How to Approach the Question
- First, identify the key term in the question: 'non-parametric test'.
- Define 'non-parametric test' in your mind: a statistical method that does not require the data to fit a normal distribution. These are often used with categorical (nominal) or ranked (ordinal) data.
- Contrast this with a 'parametric test', which assumes the data is from a normally distributed population and is typically used with continuous data (interval/ratio).
- Systematically evaluate each option against the definition of a non-parametric test.
- Recall or deduce that ANOVA, t-tests, and Z-tests are foundational parametric tests used for comparing means.
- Identify McNemar's test as the one that does not fit the parametric category, as it is used for paired nominal data, making it the correct non-parametric option.
Concept Tested & Keywords
- Concept Tested: Difference between parametric and non-parametric statistical tests.
- Stem keywords: non-parametric test, statistical test
- Lead-in keywords: Which among
- Negative lead-in flag: false
Question ID
Qul17pXG0lYA8HJJ851vSb
Reference Book
E6 NursingResearch Suresh Sharma 5e Part 2 pp. 171-173, 59-61
E6 NursingResearch Sukhpal Kaur p. 487-489
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