DNP 830 Topic 4 DQ 2: clinical vs statistical significance
A p value above .05 does not mean nothing happened. This post is about defending a change that matters to patients when the arithmetic will not certify it — and about knowing when that defence is honest.
Editorial process
Last reviewed · August 15, 2026
Two kinds of significance, and only one is calculated
The prompt is built around a real and uncomfortable situation: your DPI project may not reach statistical significance, and you will still need to say something defensible about it. Start by being precise about what a p value is. It is the probability of observing a result at least as extreme as yours if there were truly no effect. It is not the probability that your intervention worked, and .07 is not a near miss on a threshold — it is a statement about how surprising your data would be under one assumption. Small practice-improvement projects frequently fail to reach .05 because they are underpowered, and an underpowered study that finds nothing has established very little either way. This is why the sample size calculation belongs in the same post: it tells you in advance how large an effect your project could have detected, and one powered only for an implausible effect was never going to reach significance.
Clinical significance asks a different question: is the size of the change large enough to matter to a patient or to a service. That question is answered with effect sizes, absolute differences and confidence intervals, not with p values, so report those. A fall in readmissions from eleven to seven on a unit is a real difference to seven households whatever the test says, and a confidence interval that runs from a substantial benefit to a small harm tells the reader far more than a single verdict of not significant. Where this argument goes wrong is when it becomes a way of rescuing any result at all. Be explicit that a clinically important change with a wide interval is a reason to keep looking rather than a finding, and say what a properly powered study would need. That is the difference between interpreting an underpowered result and explaining one away.
Likely learning objectives
Inferred from the brief — check these against your own rubric.
- 01State correctly what a p value does and does not report.
- 02Distinguish clinical from statistical significance by the question each answers.
- 03Use effect sizes and confidence intervals to support a clinical claim.
- 04Recognise when a clinical-significance argument is being used to rescue a null result.
Read the full question
Review every instruction before using the planning guidance that follows.
Turn the brief into deliverables
- 01A sample size calculation with its inputs stated.
- 02An accurate account of what your p value would mean.
- 03An effect size or absolute difference with a confidence interval.
- 04An honest boundary on what an underpowered result can support.
Compute the size, then argue the meaning
The sample size calculation
Compute the required sample and state every input assumption.
What a p value reports
Define statistical significance accurately.
Clinical significance and how it is evidenced
Show what effect sizes and intervals contribute that p values cannot.
Where the argument stops
State the limits of interpreting an underpowered null result.
Sources on effect size and clinical importance
Recommended databases
- StatPearls statistics chapters
- PubMed Central
- Your SPSS or G*Power materials
- CINAHL
Search sequence
- 1.Find the minimal clinically important difference for your outcome if one has been published.
- 2.Look up how power, effect size and sample size trade against each other before calculating.
- 3.Read one paper that reports a non-significant result honestly and note how it is framed.
- 4.Check the correct interpretation of a confidence interval that crosses no effect.
Reference shortlist
These are authoritative starting points, not a ready-made bibliography. A qualified reviewer must confirm that each source fits the assignment and supports the claim beside which it is cited.
Nothing here is cleared for citation until you have read it.
- 01
Statistical Significance
StatPearls, NCBI Bookshelf · 2023
Defines statistical significance precisely, which is what stops the p value being described as the probability the intervention worked.
- 02
Hypothesis Testing, P Values, Confidence Intervals, and Significance
StatPearls, NCBI Bookshelf · 2023
Covers p values and confidence intervals together, which is the pairing the clinical-significance argument depends on.
- 03
Type I and Type II Errors and Statistical Power
StatPearls, NCBI Bookshelf · 2023
Explains power and Type II error, which is why a small project's null result establishes so little.
- 04
Practical guide to calculate sample size for chi-square test in biomedical research
Journal of Family Medicine and Primary Care · 2025
A worked sample size calculation showing which inputs must be declared for the result to be checkable.
Review before submission
Common mistakes
- Describing a p value as the probability the intervention worked.
- Treating clinical significance as a way of salvaging any non-significant result.
- Reporting a p value with no effect size beside it.
- Running the sample size calculation without stating the effect size it assumed.
Submission checklist
- Is your definition of the p value technically correct?
- Did you report an effect size and interval, not just significance?
- Are the inputs to your sample size calculation stated?
- Have you said where the clinical-significance argument stops being honest?
Use this guide to plan and review your own work. Follow your institution's rules and read our academic-integrity policy.

Written by
Aaron Bishop
MA, Education
assignment interpretation and research-methods coaching across disciplines
Aaron leads the EssayCrackers editorial desk. He works on how assignment briefs are read — what a rubric is actually asking for, and where students most often answer a different question than the one set.

Reviewed by
Dr. Nathan Cole
PhD, Rhetoric & Composition
Argumentation and thesis development
Nathan teaches first-year composition and directs a university writing center. He reviews EssayCrackers guides for argumentative soundness and citation accuracy.