| Course | HS345 Biostatistics |
|---|---|
| Unit | Unit 3 |
| Paper type | Data display critique |
| Length | About 600 words, 5 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | BS in Health Science |
| Updated | October 2026 |
Free sample paper for HS345 Unit 3
Accurate to the Digit, Misleading in the Message: A Unit 3 Data Display Critique of Two Charts in a Hospital Falls Report
[Student Name]
Purdue University Global
HS345: Biostatistics
Unit 3 Data Display Critique
[Instructor Name]
[Date]
The hospital, its report and every figure are composite; the graphical perception study and the statistics text cited are real.
Chart 1: Falls by Unit
The original is a bar chart of raw fall counts over twelve months for four inpatient units, sorted tallest first, under the headline "Medical unit leads in falls." The bars read: medical, 34; rehabilitation, 20; behavioral health, 18; surgical, 17. The medical unit's bar towers over the rest.
The numbers are correct, but counts compare units of very different sizes. The medical unit had about [9,440] patient days in the year, more than twice as many as rehabilitation, which had about [2,940]. More patients over more days means more chances to fall. A fair comparison divides by exposure (Pagano et al., 2022).
The redraw shows falls per 1,000 patient days:
Rehabilitation: 6.8. Behavioral health: 4.5. Medical: 3.6. Surgical: 2.1.
Sorted again, the medical unit drops to third. Rehabilitation, where patients are relearning to walk and transfer, now leads, which matches what clinicians would expect, since rehabilitation patients are encouraged to get up and walk as part of their recovery.
The original chart answered how many falls each unit had, but the committee needed to know where a patient was most likely to fall, and only a rate can answer that.
Chart 2: The Monthly Line
The second chart plots the hospital-wide fall rate by month. Its vertical axis starts at 3.0, not zero, and the line drops from 4.1 in January to 3.6 in June. On that axis the drop fills most of the chart's height and looks like a collapse. The headline reads "Falls cut sharply after new bed alarms."
The redraw starts the axis at zero. The same decline now looks like what it is, a modest dip of about 12 percent. Starting a line chart above zero is not always wrong, since it can help show small changes in a measure that never approaches zero, but the axis break must be marked and the headline must not exaggerate (Bland, 2015).
The second problem is the comparison itself. Six months of a rate based on a few dozen falls a month will wander up and down by chance. The redraw adds the previous year's months in light gray, and January to June of the prior year shows a similar dip, which suggests a seasonal pattern rather than an effect of the bed alarms.
How Readers Judge Charts
Experiments on graphical perception found that readers compare positions on a shared scale with less error than they compare lengths, angles or areas (Cleveland & McGill, 1984). Both redraws keep that strength: bars share a zero baseline, and the line shares a full axis. Neither uses three-dimensional effects, which make lengths harder to read.
Which Chart Should the Committee Believe?
Neither original. The redrawn bar chart answers a real question about risk by unit and should guide where fall prevention effort goes first, which is rehabilitation. The redrawn line chart shows a small improvement that cannot yet be credited to the bed alarms, since a similar seasonal dip occurred the year before. The committee should ask for the monthly rate over at least two years before drawing any conclusion about the alarms, ideally plotted as a run chart with its median marked, so that a genuine shift can be told from ordinary month-to-month variation.
References
Bland, M. (2015). An introduction to medical statistics (4th ed.). Oxford University Press.
Cleveland, W. S., & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387), 531-554. https://doi.org/10.1080/01621459.1984.10478080
Pagano, M., Gauvreau, K., & Mattie, H. (2022). Principles of biostatistics (3rd ed.). CRC Press.
HS345 Unit 3 instructions, in plain terms
The third unit usually presents one or more published or composite charts and asks you to explain how each could mislead, redraw it correctly and state what the data actually support. Two to three pages with before and after figures is common. Strong critiques name the specific flaw, such as counts compared without a denominator or an axis that exaggerates change, explain its effect on a reader's judgment and show a corrected version. Many sections also ask which chart a decision maker should trust. Describe each redraw in words in case figures do not display. Cite a source on how people read graphs and a statistics text for the correct measure.
Inside the HS345 Unit 3 example
Each original chart is described with its headline, then examined. For the bar chart, the critique shows why raw counts favor large units, gives the patient days and redraws the bars as rates per 1,000 patient days, re-sorted; the highlighted sentence contrasts the question each version answers. For the line chart, it explains the truncated axis, redraws it from zero and adds the previous year to expose a seasonal pattern. A section summarizes the graphical perception experiments behind both redraws. The closing section tells the quality committee which redrawn chart should guide effort and asks for two years of monthly data as a run chart before judging the alarms.
Reading the HS345 Unit 3 grading rubric
Display critiques are judged chiefly on identifying each flaw precisely and on the quality of the redraw. Precision means naming the mechanism, such as a missing denominator or a truncated axis, rather than calling a chart confusing. Redraw credit goes to corrected versions that answer the decision maker's real question. Additional points come from interpreting what the corrected data show, including the limits of a short time series. A cited principle about chart reading and a correct epidemiologic measure add evidence. Clear labels, units and captions on every figure, and a closing recommendation, complete what most graders look for. Redraws described only in words still earn credit if the axis, the measure and the ordering are spelled out exactly.
HS345 Unit 3 help: mistakes that cost points
A typical shortfall is criticizing colors or fonts while missing the denominator problem that changes the ranking. Another is redrawing a chart without saying what question the new version answers. Some students declare every non-zero axis wrong, which is not true for all line charts. Others accept a before and after drop as evidence that an intervention worked without checking earlier years. Forgetting to give the patient days or other denominators makes the redraw impossible to verify. Working from a chart in your own report? Remove identifying details, share it with the headline it carried, and the critique and redraw can be built from there. A redraw that changes two things at once, the measure and the chart type, can also hide which change mattered.
Get an HS345 Unit 3 example written to your instructions
Charts vary by section, and a custom critique works on whichever ones come with your HS345 Unit 3 assignment, or on workplace charts stripped of identifying details. Include the critique instructions and the rubric along with them. Free for a first order and delivered in 24-48 hours, the custom critique names each fault, measures the distortion it creates and redraws the display. 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.
More HS345 papers and BS in Health Science samples
- HS345 Unit 1: Average Versus Distribution Post
- HS345 Unit 2: Descriptive Statistics Exercise
- HS345 Unit 4: Probability Problem Set
- HS345 Unit 5: Sampling Distribution Exercise
- HS345 Unit 6: Confidence Interval Problem
- HS345 Unit 7: Seminar Reflection
- HS345 Unit 8: Hypothesis Test Writeup
- HS345 Unit 9: Regression Exercise
- HS345 Unit 10: Statistical Analysis Report
- HS340 Unit 3: Study Design Appraisal
- HS315 Unit 3: Core Function Analysis
- HS450 Unit 3: SWOT Analysis
- HS305 Unit 3: Research Source Evaluation
HS345 Unit 3 questions, answered
Why use patient days instead of admissions as the denominator?
Because falls happen during time spent in a bed, and units differ in how long patients stay. A rehabilitation unit keeps patients for weeks, so its exposure per admission is far larger than a surgical unit's. Falls per 1,000 patient days is the denominator most hospital fall reporting uses, and the critique explains the choice in one sentence.
Is a truncated axis always wrong?
Not always. For a line tracking small changes in a measure that never approaches zero, such as body temperature, a narrower axis can be reasonable if it is clearly labeled. The problem arises when the cut exaggerates a change somebody will act on. The example argues that a fall rate sits close enough to zero that its axis should start there.
Can I critique a chart from my own hospital?
Often, yes, if the prompt allows it. Remove unit names, dates or anything that could identify patients or staff, and describe the data source in general terms. A custom sample can work from a chart you supply with identifiers stripped, and the same four questions apply to any display, from dashboards to annual reports.
Where can I find a free HS345 Unit 3 sample paper?
The complete Unit 3 critique is available on this page, redrawing a falls bar chart and a truncated line chart from a hospital report.
Why compare units by falls per 1,000 patient days?
Because units differ in size and length of stay. A rate per patient day compares the chance of a fall during a day in each unit, which raw counts cannot do.