Most DNP students know they need a results section and a section interpreting what the results mean. The confusion is almost always about which belongs where. Results go in the results section: the data itself, the numbers, the charts, the measures, reported factually and without interpretation. What those results mean, how they connect to the practice problem, the theoretical framework, and the evidence base, belongs in the discussion or findings section that follows. Keeping those two things separate is not a stylistic preference; it is a structural requirement that SQUIRE 2.0, the reporting standard for quality improvement work, makes explicit in its item-level requirements.
This guide covers what belongs in the DNP project results section and why, how SQUIRE 2.0 structures those requirements, what clinical significance means and how it differs from statistical significance, and the five errors that account for most weak results sections. If you are still building the framework for the results section's content, our guide to how to write a DNP prospectus covers how the analysis plan in the prospectus should have pre-specified exactly what would be reported here, while our DNP project support walks you through both the data analysis stage and the written reporting stage.
Quick Answer:
DNP project results report the data: pre-intervention and post-intervention outcome measures, process measures, adherence and compliance data, contextual factors that interacted with the intervention, unintended consequences, and details about missing data. They present this information factually and over time, typically using run charts or descriptive statistics, rather than through inferential hypothesis-testing. Findings or interpretation, what the results mean in the context of the practice problem, the evidence, and the theoretical framework, belong in the discussion section. The AACN 2015 DNP white paper states that clinical significance is as important in guiding practice as statistical significance is in evaluating research, and that principle governs how a DNP results section is structured and evaluated.
Why the Distinction Matters
The separation between results and findings is not arbitrary. Results are evidence; findings are argument. A results section that contains interpretation before the evidence is fully presented is making a claim before showing the data, which inverts the logical structure of scholarly reporting. A findings or discussion section that simply restates the numbers without interpreting them is not doing the analytic work a doctoral product requires. Keeping them separate forces the writer to do both jobs fully: present the evidence completely and then interpret it rigorously.
For a DNP project, this distinction also maps onto a fundamental difference between what QI work produces and what research produces. A QI project generates local data about a local change; its results are specific to the setting, population, timeline, and context. A research study generates data intended to contribute to generalizable knowledge. The SQUIRE 2.0 guidelines make this clear: "Doing an improvement project is fundamentally different from studying it. The primary purpose of doing improvement is to produce better local processes and outcomes rather than contribute to new generalizable knowledge." That difference determines how results are reported and why the inferential testing framework used in research is usually inappropriate for a DNP QI results section. Our guide to QI versus research versus EBP covers that boundary in detail, including the specific criteria that distinguish a QI determination from an IRB review requirement.
Table 1: Results vs Findings — What Each Section Contains in a DNP Project
Dimension | Results section | Findings / Discussion section |
|---|---|---|
Primary question answered | What happened? What did the data show? | What does it mean? What are the implications for practice? |
Tone and approach | Factual, objective, data-forward; no interpretation or argument | Interpretive, argumentative; connects data to problem, literature, and framework |
What is reported | Pre- and post-intervention values; process measures; compliance rates; contextual factors; unintended consequences; missing data; data displays (run charts, SPC charts, tables) | Interpretation of what the results mean; comparison to existing evidence and benchmarks; explanation of unexpected findings; implications for sustainability and future practice |
Governing standard | SQUIRE 2.0 item 13; clinical significance over statistical significance (AACN 2015 white paper) | SQUIRE 2.0 items 14–15 (Summary and Interpretation); connection to practice-change literature and framework |
Common error | Reporting only the outcome comparison, omitting process measures, contextual factors, or unintended consequences; including interpretation before the data is presented | Restating the data without interpretation, over-claiming causation, and not connecting back to the PICOT question and theoretical framework |
What the Results Section Should Contain
The DNP project results section is not a summary. It is a complete, ordered presentation of what happened, including how the implementation evolved, what was measured, what the context was, and what went wrong, as well as what went right. SQUIRE 2.0's Results item (item 13) specifies six sub-requirements that define the scope of this section.
First, it requires a description of the initial steps of the intervention and their evolution over time, including any modifications made during the project. This means the results section should document not just the final state of the intervention but how it unfolded, perhaps as a timeline or a process table showing what was planned, what was implemented, and what was adjusted. Second, it requires details of the process measures and outcomes: what was tracked, what the baseline values were, and what the post-intervention values were. Third, it requires contextual elements that interacted with the intervention: staffing changes, a policy update, a patient census spike, anything that plausibly shaped the outcomes. Fourth, it requires observed associations between outcomes, interventions, and those contextual elements. Fifth, it requires unintended consequences, both positive and negative, including unexpected benefits, problems, failures, and costs. Sixth, it requires details about missing data.
Students who write results sections covering only the pre/post outcome comparison are meeting perhaps two of those six sub-requirements. A results section that meets all six is materially more complete and more defensible.
Process Measures vs Outcome Measures: Both Are Required
One of the most common gaps in DNP project results sections is the omission of process measures. Process measures track whether the intervention was actually delivered as intended. An outcome measure tracks whether something improved. If the outcome did not improve, a results section with process measures can show whether the intervention was implemented with adequate fidelity, which is a different and more useful finding than simply noting the outcome did not change. If the outcome did improve, process measures establish the credibility of the causal story: the intervention was delivered, and the outcome changed.
For a sepsis bundle QI project, for example, the outcome measure might be time to antibiotic administration. The process measures would include bundle completion rates, the proportion of nurses who completed the training, and the number of sepsis alerts generated versus the number of complete bundles initiated. Reporting the outcome alone tells you what happened; adding the process measures tells you how and why. Our guide to DNP project topic selection covers how to choose an outcome that is already measurable at the site, which is the prerequisite for this section. Our live DNP data analysis guide covers how run charts and SPC charts are constructed and interpreted for QI results.
How to Display QI Results: Run Charts and Descriptive Data
QI results are data over time, not a single snapshot. A pre-post comparison showing a mean value before and after the intervention answers a narrow question: was the average different? But it does not show whether the change was sustained, whether it appeared immediately or gradually, or whether it was driven by one anomalous data point. A run chart, which plots each data point in the sequence it was collected, shows all of those things and allows the application of run chart rules to distinguish special-cause variation from common-cause noise.
Run chart analysis, along with statistical process control charts for projects with sufficient data points, is the appropriate display format for most DNP QI results. Descriptive statistics, including mean, median, and range, summarize the data within a time period. Compliance rates expressed as percentages, tracked over time, show implementation fidelity. None of these requires inferential testing, and a committee familiar with QI methodology will find them more informative than a t-test. The AACN white paper's principle is operative here: clinical significance is as important as statistical significance, and a change that matters to patients or the organization, even if underpowered for a p-value, is worth describing with care and precision. Our MSN capstone support helps you structure your data display so the presentation is appropriate for the master's degree level and the program's specific format requirements. Additionally, our DNP project support provides the same for doctoral-level work, including alignment to SQUIRE 2.0 and the program's evaluation criteria.
Clinical Significance: The Governing Standard
The AACN 2015 white paper states that clinical significance is as important in guiding practice as statistical significance is in evaluating research. That statement is not a consolation for projects that could not achieve statistical significance; it is a framework principle that defines what quality improvement results should be evaluated against.
Clinical significance asks: Does this change matter to the patient, the clinician, or the organization? A 30 percent reduction in catheter-associated urinary tract infections is clinically significant whether or not a p-value threshold was reached in a small unit-level project. A two-point improvement in a pain scale from 7 to 5 is clinically meaningful even if the confidence interval crosses zero. The results section of a DNP project should report the magnitude of change in terms that are meaningful to the practice setting, not in terms of significance thresholds calibrated for research samples. This means reporting absolute change, percentage change, and the clinical context that makes the magnitude meaningful: the baseline rate, the benchmark, and the national comparison if one exists.
Table 2: SQUIRE 2.0 Item 13 — What the Results Section of a DNP Project Must Contain
SQUIRE 2.0 sub-requirement | What it means for a DNP project | Common omission |
|---|---|---|
13a: Intervention steps and evolution, including modifications | A timeline or table showing what was implemented, when, and what was changed during the project — not just the final protocol | Reporting only the final protocol, omitting adjustments made mid-implementation |
13b: Process measures and outcomes | Both the outcome data (did the clinical measure improve?) and the process data (was the intervention delivered as planned?); with values over time, not just summary means | Reporting outcome data only; omitting compliance and adherence tracking |
13c: Contextual elements that interacted with the intervention | Staffing changes, policy updates, patient census fluctuations, co-interventions, or any environmental factor that plausibly affected outcomes during the project period | No mention of contextual factors; presenting the results as if the setting were a controlled environment |
13d: Observed associations between outcomes, intervention, and context | A description of the relationship between what was done, what happened in the context, and what the outcome data showed; without claiming causation, the design cannot support | Either claiming causation ("the intervention caused the improvement") or ignoring the association entirely |
13e: Unintended consequences | Unexpected benefits, problems, failures, or costs — including effects on staff workload, unintended patient impacts, or implementation barriers that were not anticipated | Reporting only favorable outcome data; omitting problems, costs, or unintended effects |
13f: Missing data | A description of what data were expected but not obtained, why, and what effect the gaps may have on the interpretation of results | No acknowledgment of missing data; presenting incomplete datasets as if complete |
What Belongs in Findings vs Results
Once the results section is complete, the findings section or discussion takes up the interpretive work. The discussion asks what the results mean, not what they were. It connects the outcome data to the practice problem identified in the prospectus, explains whether the results support the theoretical framework that grounded the design, compares the findings to the existing literature, and identifies the implications for practice going forward.
The boundary between a DNP project discussion and a PhD dissertation discussion is similar in structure but different in purpose. A PhD discussion argues about generalizable implications and suggests future research. A DNP discussion argues about the sustainability and scalability of the change in the local setting, and its implications for practice improvement, not for the research literature. That framing comes directly from the white paper's characterization of DNP knowledge as "transferable but not generalizable," which our guide to MSN vs DNP project differences places in the context of the two degrees' distinct purposes. Our results and analysis support help you structure the results section in the format their committee expects, while our discussion chapter support covers the interpretive section that follows, where the data is connected to the practice problem, the literature, and the framework.
Working on your results section and not sure what should be there? |
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The difference between a results section that meets SQUIRE 2.0's six sub-requirements and one that only covers the outcome comparison is the difference between a document your committee accepts and one it sends back for expansion. A specialist can review your data, help you structure the presentation in line with SQUIRE 2.0, and identify what is missing before your submission. Send your results section for review, and you will have an itemized quote within 2 to 4 business hours, no obligation. |
The Five Common Errors
Five errors account for most weak DNP project results sections, and all five are fixable once they are named.
The first is reporting only descriptive data with no process measures and no over-time display. A mean pre-intervention value and a mean post-intervention value are necessary but not sufficient. SQUIRE 2.0 requires the timeline of implementation, the process measures, and the contextual factors as well.
The second is conflating statistical significance with clinical significance. A p-value in an underpowered QI project is usually uninformative, and chasing one by adding inferential language to a QI results section signals a misunderstanding of what the project is measuring. The relevant measure is whether the change is large enough to matter to the setting.
The third is overclaiming causation from a pre-post QI design. A pre-post design with no control group cannot establish that the intervention caused the change. The language in the results section should reflect what the design can actually support: the outcome measure changed by X percent following implementation of the intervention, during a period when the following contextual factors were present. That is not a weak claim; it is an accurate one.
The fourth is omitting unintended consequences. SQUIRE 2.0 specifically requires reporting unexpected benefits, problems, failures, and costs. A results section that reports only the favorable outcome data is incomplete and may actually undermine the project's credibility.
The fifth is failing to link the results back to the PICOT question and the project framework. The results section should close the loop opened by the clinical question: the PICOT question stated what would be measured, and the results section reports whether it was measured and what was found. Our guide to identifying the DNP practice gap emphasizes why the gap needs to be measurable from the beginning. Our comprehensive DNP project guide covers how each phase of the project, from PICOT through final defense, connects to the next.
How This Section Relates to the Overall Project
The results section sits at the hinge point of the project: it is where the implementation that preceded it produces evidence that the discussion section interprets. A results section that does not contain enough evidence, or does not present it clearly enough, leaves the discussion with nothing to work with. A results section that is overloaded with interpretation has already made arguments that the discussion section cannot revisit.
Setting this up correctly from the beginning of the project, by pre-specifying the data to be reported in the prospectus and the PICOT-D question, is what makes a clean results section possible. Our EBP and PICOT support helps you if you are still working through the evidence base that contextualizes the results before writing them, and our capstone projects hub is the starting point if you are looking for an overview of what support is available at each stage of the project.
Frequently Asked Questions
What is the difference between results and findings in a DNP project?
Results are the data themselves, reported factually and without interpretation: the pre- and post-intervention values, the process measures, the adherence rates, the contextual factors, and the unintended consequences. Findings are the interpretations: what the results mean in the context of the practice problem, the theoretical framework, and the existing literature. Results belong in the results section; findings and interpretation belong in the discussion.
Does the DNP project results section need statistical tests?
Most DNP QI projects do not benefit from inferential statistical testing. The design is typically a pre-post QI study with no control group, which means a significance test cannot establish causation, and an underpowered sample makes the p-value uninformative. The appropriate analysis for most DNP results is descriptive statistics and run chart or statistical process control chart analysis, which show the data over time and allow application of run chart rules to detect meaningful change. Some programs require a statistical test; always check your program's specific requirements.
What is clinical significance, and why does it matter more than statistical significance in a DNP project?
Clinical significance asks whether the change is large enough to matter in practice: to the patient, the clinician, or the organization. Statistical significance asks whether the result is unlikely to be due to chance, given the sample size and design. In a small unit-level QI project, statistical significance is often unachievable even when a clinically meaningful change occurred. The AACN 2015 white paper explicitly states that clinical significance is as important as statistical significance for DNP projects, which means the results section should focus on the magnitude and the clinical context of the change, not on p-values.
What is SQUIRE 2.0, and does my DNP project need to follow it?
SQUIRE 2.0 (Standards for Quality Improvement Reporting Excellence) is the reporting guideline for QI work, maintained at squire-statement.org and published in BMJ Quality and Safety in 2016. It is the standard for QI manuscripts in most nursing journals, and many DNP programs either require or strongly recommend it as the framework for the final project report. Even if your program does not mandate it, its item-level requirements provide the most complete checklist available for ensuring the results section contains what it should.
How long should the results section be?
Length depends on the scope of the project and the program's format requirements, but the results section should be long enough to meet SQUIRE 2.0's six sub-requirements for item 13. A results section that covers only the pre/post outcome comparison typically runs one to two pages and is incomplete. A results section that includes the intervention timeline, the process measures over time, the contextual factors, the outcome data in run chart form, and the unintended consequences typically runs three to five pages. A worked table or run chart can carry substantial data in a compact format, which is why data displays are not just presentational choices but structural requirements.
You May Also Find Useful
- How to Write a DNP Prospectus: Alignment First
- MSN Project vs DNP Project: What the Difference Really Means
- QI vs Research vs EBP: Does Your DNP Project Need IRB Review?
- DNP Project Data Analysis: Run Charts, Statistical Process Control, and Pre/Post Comparisons
- DNP Practice Gap: Why Committees Reject It and How to Fix It
The Evidence Comes First, Then the Argument
The results section presents the evidence. The discussion makes the argument. Keeping those two sections separate and doing both fully is what makes the project's findings credible. The results section should be complete enough that a reader who stopped there could understand what happened. The discussion should be interpretive enough that a reader who has read only it would know what the evidence means and why it matters for practice. Neither section can do the other's work, and a project whose two sections are well-separated and well-developed is a project whose committee will find easy to evaluate.
If you would like a specialist to review your results section or help structure your data display before submission, share what you are working on, and you will have an itemized quote within 2 to 4 business hours, no obligation.
