| Course | NUR 520 Epidemiological and Biostatistical Applications in Healthcare |
|---|---|
| Module | Module 5 |
| Paper type | Statistical inference and interpretation paper |
| Length | About 1,040 words, 6 pages |
| Format | APA 7 student paper |
| School | Southern New Hampshire University |
| Program | MSN |
| Updated | September 2026 |
Free sample paper for NUR 520 Module 5
Highest, or Just High? Confidence Intervals and What Can Be Claimed About COPD in Coös County
[Student Name]
Southern New Hampshire University
NUR 520: Epidemiological and Biostatistical Applications in Healthcare
Module Five Assignment
[Instructor Name]
[Date]
Highest, or Just High? Confidence Intervals and What Can Be Claimed About COPD in Coös County
Milestone One described Coös County as having New Hampshire's highest estimated COPD prevalence. A point estimate, however, is only the most likely value; the true prevalence could be somewhat higher or lower. This paper uses the confidence intervals that accompany CDC's PLACES 2025 county estimates to ask a sharper question: which differences between Coös and other counties are clear, and which could reflect uncertainty in the estimates? The answer is more modest than the ranking suggests: Coös's age-adjusted COPD prevalence is clearly higher than the lowest county's, but its intervals overlap with those of most other counties, so the claim that it is the single highest should be softened.
What a Confidence Interval Means
A 95% confidence interval is a range produced by a method that, if the whole process of sampling and estimation were repeated many times, would contain the true value in about 95% of repetitions. It expresses the precision of an estimate. Wide intervals signal less certainty, often because of small samples; narrow intervals signal more. Greenland et al. (2016) caution against common misreadings: a particular 95% interval does not have a 95% probability of containing the true value in any simple sense, and values just outside the interval are not dramatically less compatible with the data than values just inside. For practical use, the interval is best read as the range of values reasonably compatible with the data and the model.
The County Intervals
Table 1 lists age-adjusted COPD prevalence and its 95% confidence interval for each county, ordered from highest to lowest point estimate (Centers for Disease Control and Prevention [CDC], 2025). Age-adjusted figures are used because the question is whether risk differs between counties, not how many people are affected.
Table 1
Age-Adjusted Prevalence of Diagnosed COPD Among Adults, With 95% Confidence Intervals, New Hampshire Counties
| County | Age-adjusted prevalence (%) | 95% confidence interval |
|---|---|---|
| Coös | 7.1 | 5.9 to 8.5 |
| Sullivan | 6.2 | 5.1 to 7.5 |
| Cheshire | 5.7 | 4.6 to 6.9 |
| Belknap | 5.7 | 4.6 to 6.9 |
| Strafford | 5.5 | 4.5 to 6.6 |
| Merrimack | 5.3 | 4.3 to 6.4 |
| Grafton | 5.3 | 4.3 to 6.4 |
| Carroll | 5.2 | 4.3 to 6.4 |
| Hillsborough | 4.9 | 4.0 to 6.0 |
| Rockingham | 4.6 | 3.7 to 5.5 |
Note. Data from CDC PLACES, 2025 release, model-based estimates.
Reading the Overlap
The lower limit of Coös's interval, 5.9%, lies above the upper limit of Rockingham's, 5.5%. When two 95% intervals do not overlap at all, the difference between the estimates is statistically significant at about the 5% level or stronger, so the gap between Coös and Rockingham is unlikely to be explained by chance in the estimation. Every other county's interval overlaps with Coös's. Hillsborough's upper limit, 6.0%, only just reaches Coös's lower limit, while Sullivan's interval, 5.1% to 7.5%, overlaps substantially.
Overlap needs careful interpretation. Schenker and Gentleman (2001) showed that checking whether confidence intervals overlap is a conservative way to test a difference: when intervals do not overlap, the difference is significant, but intervals can overlap and the difference can still be significant when tested directly. So the overlap between Coös and Hillsborough, for example, does not show that the two counties have the same prevalence; it shows only that this simple check cannot establish a difference. A formal test of the difference between the two estimates, using their standard errors, would be needed.
Connecting Intervals to Hypothesis Tests
A confidence interval and a hypothesis test express the same information in different ways. For the question of whether Coös's prevalence differs from Rockingham's, the null hypothesis is that the two counties have the same true prevalence and the alternative is that they differ. A 95% interval for the difference that excludes zero corresponds to rejecting the null hypothesis at the 5% significance level, with a p-value below 0.05. Two errors are possible. A type I error would be concluding that the counties differ when they do not; a type II error would be failing to detect a real difference, which is likely when intervals are wide, as they are for small counties. The wide intervals for Coös and Sullivan mean that a real difference between them could easily go undetected.
Model-Based Estimates and Uncertainty
One more caution applies. The PLACES intervals describe uncertainty in a model-based estimate, not the sampling error of a simple survey in each county. The model borrows information from counties with similar characteristics, which tends to pull small-county estimates toward the average and may make their intervals narrower than a direct survey could produce. The intervals are therefore useful for honest comparison, but they are not a substitute for local data if the county needs precise figures to evaluate a program.
Revising the Problem Statement
Milestone One stated that Coös has the highest estimated COPD prevalence in the state. The intervals support a more careful version: Coös has the highest point estimate of age-adjusted COPD prevalence among New Hampshire counties, clearly higher than the lowest county and within the upper group of counties, with an estimated 7.1% (95% CI 5.9% to 8.5%). The same analysis for smoking tells a similar story; Coös's age-adjusted smoking estimate is the highest point estimate in the state, at 15.6% (12.4% to 19.1%), with an interval that overlaps several others (CDC, 2025). The revised statement is less dramatic but more defensible, and it still supports prioritizing the county.
Why This Matters for Nurse Leaders
Nurse leaders use numbers to argue for resources. An argument built on an overstated ranking is easy for a skeptical reader to dismiss, especially when the intervals are published beside the estimates. An argument that reports the estimate, its interval and what can and cannot be concluded is harder to dismiss and more likely to earn trust. For Coös, the stronger case rests on the combination of a high COPD estimate, a high smoking estimate, older age structure and barriers to care, not on the claim of being first by a fraction of a percentage point.
Conclusion
Confidence intervals turn a county ranking into a more honest set of comparisons. Coös's COPD prevalence is clearly higher than the lowest county's and sits at the top of an upper group, but the evidence does not establish that it is higher than every other county. Reporting estimates with their intervals, and choosing words that match them, protects the credibility of every recommendation that follows.
References
Centers for Disease Control and Prevention. (2025). PLACES: County data (GIS friendly format), 2025 release [Data set]. https://data.cdc.gov/d/i46a-9kgh
Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: A guide to misinterpretations. European Journal of Epidemiology, 31(4), 337-350. https://doi.org/10.1007/s10654-016-0149-3
Schenker, N., & Gentleman, J. F. (2001). On judging the significance of differences by examining the overlap between confidence intervals. The American Statistician, 55(3), 182-186. https://doi.org/10.1198/000313001317097960
What the NUR 520 Module 5 instructions ask for
The Module 5 assignment in NUR 520 usually asks you to apply inferential statistics to your population health problem. Common prompts ask you to explain confidence intervals, hypothesis testing, p-values and statistical significance, interpret intervals or test results for your data, discuss type I and type II errors and explain the implications for practice. Some sections provide a dataset or statistical output to interpret; others ask you to use published estimates with their intervals. Expect a short paper of about three pages in APA 7, often with a table. The core skill is interpretation, so focus on what the numbers allow you to conclude and, just as important, what they do not. A short table of estimates with their intervals usually makes that discussion much easier to follow.
How this NUR 520 Module 5 statistical inference paper example is built
This example tests an earlier claim, that Coös County has New Hampshire's highest COPD prevalence, against the 95% confidence intervals CDC publishes with its county estimates. It defines a confidence interval precisely, cites a methods paper on common misinterpretations, presents all ten counties' age-adjusted estimates with intervals, and reads the overlap correctly, including why overlap does not prove equality. It then connects intervals to hypothesis tests and both error types, explains how model-based estimation affects the intervals, and rewrites the problem statement to match the evidence, closing with why that honesty matters for nurse leaders who must argue for resources in front of skeptical audiences.
Where the NUR 520 Module 5 rubric puts the points
Inference assignments are typically graded on the accuracy of statistical definitions, the correctness of the interpretation, the discussion of significance and error types, the application to practice and the presentation. Interpretation carries the most weight: graders look for correct statements about what an interval or p-value means and for conclusions that do not go beyond the evidence. Papers that recognize limits, such as small samples or model-based estimates, score higher than papers that treat every estimate as exact. Revising a claim in light of the analysis shows mastery, because it demonstrates that the statistics changed the writer's conclusion rather than decorating it after the fact.
NUR 520 Module 5 help: the mistakes that cost points
The weakest inference papers state that a result is significant or not significant without saying what that means for the question. Others misread intervals, claiming a 95% chance that the true value lies inside, or treating overlapping intervals as proof that two groups are the same. Some ignore the size of the difference and focus only on the p-value. Others never connect the statistics to a decision. Define each concept accurately, report estimates with their intervals, interpret overlap carefully, discuss both error types in the context of your sample size and end by stating, in plain words, what your data do and do not support.
Get NUR 520 Module 5 written to your instructions
Send the estimates, intervals or statistical output for your problem, plus the Module 5 prompt. An inference paper that interprets them correctly and says what can be claimed is written in 24 to 48 hours, and the first sample is free. The paper above is an original model document written by our desk, not a submitted student paper and not an official Southern New Hampshire University document.
More NUR 520 papers and related MSN samples
- NUR 520 Module 1 Short Paper: The Epidemiologic Triangle and COPD in Coös County
- NUR 520 Module 2 Data Sources Paper: Judging the Data Behind County COPD Estimates
- NUR 520 Module 3 Measures of Disease Frequency: Crude and Age-Adjusted COPD Across Ten Counties
- NUR 520 Module 4 Milestone One: Describing COPD and Smoking in Coös County
- NUR 520 Module 6 Study Designs Paper: Matching Designs to Questions About COPD
- NUR 520 Module 7 Milestone Two: An Analysis Plan With Relative Risk and Odds Ratios
- NUR 520 Module 8 Screening Paper: Screening Accuracy and the Case Against COPD Screening
- NUR 520 Module 9 Final Project: An Epidemiological Analysis of COPD With Population-Level Recommendations
- NUR 520 Module 10 Journal: Learning to Read Population Data as a Nurse
- NUR 508 Module 5 Competency Gap Assessment
- NUR 502 Module 1 Learning Theory Paper
- NUR 506 Module 1 Clinical Question Discussion
- NUR 315 Module 5 Milestone Two
NUR 520 Module 5 questions, answered
Where can I find a free NUR 520 Module 5 statistical inference sample?
This page carries the complete paper free: CDC county COPD estimates compared with their 95% confidence intervals, overlap read correctly, hypothesis tests and error types explained, and a revised problem statement, with three references. Interpretations of your own data can be requested.
What does a 95% confidence interval mean?
It is a range produced by a method that would contain the true value in about 95% of repeated samples. In practice, read it as the range of values reasonably compatible with the data and the model.
If two confidence intervals overlap, is there no difference?
Not necessarily. Non-overlapping intervals indicate a significant difference, but overlapping intervals can still hide a significant difference that a direct test of the difference would detect.
What are type I and type II errors?
A type I error is concluding there is an effect or difference when there is none. A type II error is missing a real effect or difference, which is more likely with small samples and wide intervals.
Should I report p-values or confidence intervals in NUR 520?
Confidence intervals are often more informative because they show the size and precision of an estimate. Report p-values when your instructions ask for them, alongside the estimate and interval.