| Course | NU704 Biostatistics, Epidemiology, and Social Determinants of Population Health |
|---|---|
| Unit | Unit 8 |
| Paper type | Significance interpretation paper |
| Length | About 860 words, 6 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | Doctor of Nursing Practice (DNP) |
| Updated | October 2026 |
Free sample paper for NU704 Unit 8
Three Intervals That Touch or Clear One: A Unit 8 Significance Interpretation Paper for a DNP Project Team
[Student Name]
Purdue University Global
NU704: Biostatistics, Epidemiology, and Social Determinants of Population Health
Unit 8 Significance Interpretation Paper
[Instructor Name]
[Date]
The project team is composite; the studies and their published results are real.
Why the Words Matter
The project team on two composite Dubuque medical-surgical units will soon present results to the nurse executive and the quality council. In early meetings, team members used "significant" to mean both "statistically detectable" and "important," and treated p = .05 as a line between real and unreal effects. The American Statistical Association's 2016 statement addressed exactly these habits. In its terms, a p-value measures only the mismatch between what was observed and what a chosen model predicts; it cannot give the chance that the team's belief is correct, it carries no information about how big an effect is, and passing .05 should never be the sole basis for a conclusion (Wasserstein & Lazar, 2016).
Result 1: Education and Readmission
Healy et al. (2013) reported an adjusted odds ratio of 0.66 for 30-day readmission among patients who received inpatient diabetes education, with an interval from 0.51 to 0.85 and a p-value of .001.
The interval excludes 1, so the data are not compatible with no association at the 95 percent level. More usefully, the interval says the association could plausibly be anywhere from a moderate reduction in the odds, to 0.85, to a large one, to 0.51. The p-value of .001 adds that, under the model and with no real association, results this extreme would rarely occur, but it says nothing about the size of the effect, which the interval conveys.
Clinically, the point estimate corresponds to readmission rates of 11 and 16 percent, a five-point difference that matters to a hospital. Statistically and clinically, this result is meaningful, with the caution that it is observational. The team should describe it as an association that supports trying education, not as proof that education will produce the same five points on its own units.
Result 2: Literacy and Tight Control
Schillinger et al. (2002) reported that patients with inadequate health literacy had an adjusted odds ratio of 0.57 for tight glycemic control, with an interval from 0.32 to 1.00 and a p-value of .05.
Team members called this "barely significant." A fairer reading is that the plausible range stretches from a sizable drop in the odds of tight control down to no change whatever. The point estimate suggests a meaningful association, but the interval is wide and touches 1. The same study found an odds ratio of 2.03 for poor control, interval 1.11 to 3.73, which clears 1 and supports the overall picture.
An interval that ends at 1.00 is not half a finding; it is a range that still includes no difference.
Result 3: Induction and Newborn Outcomes
A team member brought a different example from her women's health coursework. In a large trial of induction at 39 weeks, the composite of adverse newborn outcomes was 4.3 percent with induction and 5.4 percent with expectant management, a relative risk of 0.80 with an interval from 0.64 to 1.00 (Grobman et al., 2018). The authors reported no significant difference.
This result illustrates the reverse error. "Not significant" does not mean "no effect." The data are compatible with a 36 percent relative reduction and with no change. What the trial does show convincingly is that induction did not increase adverse outcomes, which was its main clinical question.
Statistical and Clinical Significance
Statistical significance depends on effect size and sample size. With thousands of patients, a difference too small to matter can still produce a tiny p-value; with a few dozen, a difference that matters greatly can fail to reach .05. Clinical significance asks whether a difference would matter to patients or decisions. For the insulin project, a reduction in glucose-related returns of two percentage points might reach statistical significance in a large system but might not justify the staff time on two units; a reduction of seven points might matter greatly even if the project's small sample makes the interval wide.
How the Team Will Report Results
The team agreed on five practices. Report the effect estimate with its 95 percent confidence interval for every main result. Describe what the interval includes in words, such as "compatible with a reduction of between 2 and 13 percentage points." Report exact p-values rather than thresholds. Avoid the words "significant" and "trend toward significance"; say "statistically detectable" when that is meant, and discuss importance separately. And state the design's limits next to the result, since a before-and-after comparison cannot establish cause however small the p-value.
Conclusion
The three results show three common misreadings: treating a p-value as the size of an effect, treating an interval touching 1 as half a finding and treating a non-significant result as proof of no effect. Reading intervals as ranges of plausible effects, and judging clinical importance separately, gives the team a more honest way to talk about the evidence and about its own results.
References
Grobman, W. A., Rice, M. M., Reddy, U. M., Tita, A. T. N., Silver, R. M., Mallett, G., Hill, K., Thom, E. A., El-Sayed, Y. Y., Perez-Delboy, A., Rouse, D. J., Saade, G. R., Boggess, K. A., Chauhan, S. P., Iams, J. D., Chien, E. K., Casey, B. M., Gibbs, R. S., Srinivas, S. K., ... Macones, G. A. (2018). Labor induction versus expectant management in low-risk nulliparous women. New England Journal of Medicine, 379(6), 513-523. https://doi.org/10.1056/NEJMoa1800566
Healy, S. J., Black, D., Harris, C., Lorenz, A., & Dungan, K. M. (2013). Inpatient diabetes education is associated with less frequent hospital readmission among patients with poor glycemic control. Diabetes Care, 36(10), 2960-2967. https://doi.org/10.2337/dc13-0108
Schillinger, D., Grumbach, K., Piette, J., Wang, F., Osmond, D., Daher, C., Palacios, J., Sullivan, G. D., & Bindman, A. B. (2002). Association of health literacy with diabetes outcomes. JAMA, 288(4), 475-482. https://doi.org/10.1001/jama.288.4.475
Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129-133. https://doi.org/10.1080/00031305.2016.1154108
Reading the NU 704 Unit 8 instructions
The eighth unit usually asks students to interpret p-values and confidence intervals from published studies, distinguishing statistical from clinical significance. Prompts commonly provide or ask students to select several results, including at least one that is borderline or non-significant, and expect each to be explained in plain language. Instructors reward papers that treat intervals as ranges of plausible effects, avoid describing p-values as the probability that a hypothesis is true and recognize that a non-significant result does not show absence of effect. Many sections also ask how the student will report their own project's results, which makes the paper practical. Sources usually include the 2016 statisticians' statement on p-values and the studies being interpreted, quoted exactly with their estimates and intervals, with the design of each study named.
Inside the NU704 Unit 8 example
The paper begins with the team's loose language and the statisticians' 2016 statement on what a p-value can and cannot say. Result 1 reads Healy and colleagues' odds ratio, its interval and its p-value, then translates the point estimate into a five-point difference in readmissions. Result 2 reframes Schillinger and colleagues' borderline interval as a range that still includes no change, under a highlighted line, and sets it beside the clearer poor-control result. Result 3 uses Grobman and colleagues' induction trial to show that a non-significant result still answered its clinical question, marked by a margin note. A section separates statistical from clinical significance with examples from the insulin project. A second margin note marks the team's five reporting rules, which close the paper before a short conclusion naming the three misreadings it corrects.
NU704 Unit 8 rubric: what earns full points
Significance interpretation papers earn marks for accurate statements about p-values, correct reading of intervals, distinction between statistical and clinical significance, handling of borderline and null results, and reporting plans. Accuracy requires avoiding common misstatements, such as describing a p-value as the chance the null hypothesis is true. Interval credit depends on reading each as a range of plausible effects and noting where it touches or excludes the null. Graders reward papers that judge clinical importance separately, using absolute differences and context. Borderline and non-significant results should be explained without overstating or dismissing them. A concrete reporting plan for the student's own project shows application. Exact reproduction of published estimates and intervals, clear headings and correct citation of the p-value statement and the studies complete the criteria in most sections.
NU704 Unit 8 help: mistakes that cost points
Papers lose the most marks when they repeat the language they are meant to correct, describing results as almost significant or treating p = .05 as a line between truth and falsehood. Another frequent error is concluding that a non-significant trial proved no effect. Some students read only whether an interval crosses 1 and ignore its width, missing how much uncertainty remains. Others discuss statistical significance without ever asking whether the difference would matter to patients. Leaving out a reporting plan wastes the chance to apply the lesson. Begin with one result you rely on, write its interval in words, then decide, in a separate sentence, if an effect that large would alter what you do. Keep those two sentences apart. Results from your own reading list are enough to begin a custom interpretation paper.
Get an NU704 Unit 8 example written to your instructions
Send the results you are interpreting and the paper instructions with the rubric. A custom paper explains statistical and clinical significance, intervals and chance in plain terms. The first is free, delivered within 24-48h. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
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NU704 Unit 8 questions, answered
What does a p-value tell you?
How compatible the data are with a specified statistical model, usually one of no effect. It does not measure the size or importance of an effect.
Why are confidence intervals preferred?
They show the range of effect sizes compatible with the data, conveying both size and precision.
Can a result be statistically significant but unimportant?
Yes, especially in large samples. Clinical significance depends on the size of the effect and what it means for patients.
Where can I find a free NU704 Unit 8 sample paper?
This page has the complete NU704 Unit 8 significance interpretation paper. It reads three published results with intervals ending at 0.85 and 1.00, separates statistical from clinical significance and sets five reporting rules for a project team.
What does an interval ending at exactly 1.00 mean in the NU704 Unit 8 paper?
It means the data are compatible with effects ranging from a substantial change to no change at all. The paper treats it as a range that still includes no difference, not as half a finding.