In a research study, a p-value of 0.03 was found. What does this indicate?

INI-CET EXAM -2025

Nursing Research & Statistics

Medium

Appeared in: INI-CET EXAM -2025

Explanation

A p-value represents the probability of obtaining the observed results, or more extreme results, if the null hypothesis (the hypothesis of no effect) were true.
A p-value of 0.03 means there is a 3% probability that the observed effect is due to random chance alone.
In research, a result is typically considered statistically significant if the p-value is less than the alpha level, which is conventionally set at 0.05.
Since 0.03 is less than 0.05, the result is statistically significant, and the null hypothesis is rejected.

Option B: This is a common misinterpretation. The p-value is the probability of the data, assuming the null hypothesis is true (P(data|H₀)), not the probability of the null hypothesis being true (P(H₀|data)). A low p-value suggests we should reject the null hypothesis, not be confident in it.
Option C: The result is considered statistically significant because the p-value (0.03) is less than the standard significance level (alpha) of 0.05.
Option D: This statement contains a factual error. The p-value is 0.03, which corresponds to a 3% chance probability, not a probability below 2%.

Visual 1: Diagram - A bell curve (normal distribution) showing the critical region for an alpha of 0.05. The diagram should then show where the p-value of 0.03 falls within this critical region, illustrating why the null hypothesis is rejected.
Visual 2: Flowchart - A simple flowchart showing the steps of hypothesis testing: 1. State null hypothesis -> 2. Set alpha level (e.g., 0.05) -> 3. Calculate p-value -> 4. Compare p-value to alpha -> 5a. If p <= alpha, Reject Null -> 5b. If p > alpha, Fail to Reject Null.

Clinical Relevance

Nursing practice connection: This is primarily an exam-oriented knowledge point with limited direct bedside application, so retain Interpretation of p-value and statistical significance in research as background academic context rather than a clinical decision trigger.
Nurses must be able to interpret p-values to critically appraise research articles and determine if a new intervention, treatment, or diagnostic tool is effective and supported by evidence.
Understanding statistical significance helps nurses differentiate between findings that are likely real and those that could simply be due to random chance, which is crucial for evidence-based practice (EBP).
A nurse manager might use research findings with significant p-values to justify changes in unit protocols or advocate for new equipment to improve patient outcomes.

How to Approach the Question

First, identify the core concept being tested: the meaning of a p-value.
Recall the definition: A p-value is the probability of observing the given result (or a more extreme one) purely by chance, assuming the null hypothesis of no effect is true.
Convert the given p-value (0.03) into a percentage: 0.03 = 3%.
Compare this p-value to the conventional threshold for statistical significance, which is the alpha level (α), typically set at 0.05 (5%).
Since 0.03 is less than 0.05, conclude that the result is statistically significant.
Evaluate each option: Option A correctly states the 3% chance. Option B misinterprets the p-value. Option C incorrectly claims the result is not significant. Option D uses the wrong percentage (2%).

Concept Tested & Keywords

Concept Tested: Interpretation of p-value and statistical significance in research.
Stem keywords: research study, p-value, 0.03
Lead-in keywords: What does this indicate?

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