| Course | IHP 525 Biostatistics |
|---|---|
| Module | Module 1 |
| Paper type | discussion post on interpreting p-values |
| Length | About 370 words, 3 pages |
| Format | APA 7 student paper |
| School | Southern New Hampshire University |
| Program | MPH |
| Updated | September 2026 |
Free sample paper for IHP 525 Module 1
Module One Discussion
Point Oh Seven Does Not Mean Failure
Last year, our health department in Bayview County, a composite agency, piloted a blood pressure program with 60 adults recruited through barbershops. After six months, participants' systolic pressure had fallen about 5 mm Hg more than a comparison group's, with a p-value of 0.07. My manager read the report, saw that 0.07 is bigger than 0.05 and said the program did not work. I did not know how to explain why that conclusion bothered me. This week's readings gave me the words.
Wasserstein and Lazar (2016) summarize a statement from the American Statistical Association on how p-values should be understood. A p-value describes how compatible the observed data are with a particular statistical model, such as one in which the program has no effect. It is not the probability that the program works or does not work, it does not measure how large or important an effect is and a decision should not rest on whether it falls on one side of a threshold. Our 0.07 says that data like ours would be somewhat unusual if the program truly had no effect, but not unusual enough to cross an arbitrary line.
Greenland et al. (2016) list many common misreadings of tests and intervals, and my manager's was near the top: treating a result that is not statistically significant as evidence that there is no effect. The confidence interval for our difference ran from about 11 mm Hg lower to slightly higher than usual care. That range includes no effect, but it also includes benefits large enough to matter for heart attacks and strokes. The study was simply too small to tell precisely.
Splitting results into significant and not significant at 0.05, one critique argues, throws away information and breeds both false alarms and missed effects (Sterne & Davey Smith, 2001). The authors recommend reporting estimates with confidence intervals and interpreting them in light of study size, prior evidence and plausibility.
So the honest summary is that the pilot suggests a benefit of uncertain size and deserves a larger test, not cancellation. Classmates, has a decision in your workplace hinged on whether a result crossed 0.05, and how might a confidence interval have changed it?
References
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
Sterne, J. A. C., & Davey Smith, G. (2001). Sifting the evidence: What's wrong with significance tests? Another comment on the role of statistical methods BMJ, 322(7280), 226-231. https://doi.org/10.1136/bmj.322.7280.226
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
What the IHP 525 Module 1 instructions ask for
The opening IHP 525 Discussion usually asks you to explain why statistics matter in public health and how statistical results should be interpreted, often with attention to p-values. Aim for about 350 words supported by at least two peer-reviewed sources in APA 7, and return later to respond to classmates. Use a real or composite example, explain what a p-value measures and does not measure and show how a confidence interval adds information. Avoid treating 0.05 as a bright line. Close by asking classmates to reflect on how statistical results are used where they work or study. IHP 525 graders notice clean headings in IHP 525 papers. IHP 525 names and dates need checking before IHP 525 submission.
How this IHP 525 Module 1 discussion example is built
This post comes from a county analyst whose manager wanted to cancel a barbershop blood pressure pilot because its 5 mm Hg benefit had a p-value of 0.07. Wasserstein and Lazar's summary of the ASA statement explains that a p-value reflects compatibility with a no-effect model, not the probability the program works or the size of its effect. Greenland and colleagues identify the manager's error, reading nonsignificance as no effect, and the confidence interval shows a wide range including meaningful benefit. Sterne and Davey Smith argue against fixed thresholds. The writer recommends a larger test and asks classmates about decisions driven by 0.05. IHP 525 students can reuse this structure for IHP 525 work. IHP 525 claims here trace to cited IHP 525 sources.
Where the IHP 525 Module 1 rubric puts the points
Opening discussions in IHP 525 are commonly judged on accurate explanation of statistical concepts, correct interpretation of p-values and confidence intervals, relevance of the example, use of evidence, APA 7 and contributions to others' learning. Strong posts state precisely what a p-value means, use a confidence interval to show uncertainty and connect interpretation to decisions. Posts lose points when they define a p-value as the probability the null hypothesis is true, treat nonsignificance as proof of no effect or skip the example. Replies that gently correct a common misinterpretation in a classmate's post show real command of the material. IHP 525 marks favor careful formatting across IHP 525 sections. IHP 525 citations keep every IHP 525 argument credible.
IHP 525 Module 1 help: the mistakes that cost points
In week one of IHP 525, posts often lose points for textbook definitions that repeat common misreadings, for skipping confidence intervals, for examples without numbers and for replies that only agree. A related weak spot is equating statistical significance with practical importance. Define p-values carefully, interpret a confidence interval, connect the result to a decision and pose a question others can actually answer. When the instructor has assigned a particular article or data set, add it to your IHP 525 notes so the post uses it. IHP 525 drafts start well from a IHP 525 outline. IHP 525 feedback already received guides IHP 525 revisions.
Get IHP 525 Module 1 written to your instructions
Send the IHP 525 prompt and a result you want to unpack. The post you get back explains what the p-value does and does not mean, uses a confidence interval to show uncertainty and ties interpretation to a real decision, within 24 to 48 hours, free the first time. 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.
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IHP 525 Module 1 questions, answered
Where can I find a free IHP 525 Module 1 Discussion sample?
The complete post is published here: why a p-value of 0.07 does not mean a program failed, with confidence intervals and common misinterpretations.
What does a p-value measure?
How compatible the observed data are with a specified model, such as no effect; it is not the probability that the hypothesis is true.
Does a p-value above 0.05 mean there is no effect?
No. It means the data are not incompatible enough with no effect to cross a threshold; a real effect may still exist, especially in small studies.
Why report confidence intervals?
They show the range of effect sizes compatible with the data, conveying both magnitude and precision.
Should decisions depend on crossing p < 0.05?
Statisticians advise against it; decisions should weigh effect size, precision, prior evidence, costs and plausibility.