| Course | HS345 Biostatistics |
|---|---|
| Unit | Unit 8 |
| Paper type | Hypothesis test writeup |
| Length | About 620 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 8
Hypotheses Before Numbers, Answer in Seconds: A Unit 8 Hypothesis Test Writeup for a Balance Class Trial in Older Adults
[Student Name]
Purdue University Global
HS345: Biostatistics
Unit 8 Hypothesis Test Writeup
[Instructor Name]
[Date]
The trial, its participants and its results are composite; the statistics papers and text cited are real.
Research Question and Hypotheses
Question: among community-dwelling adults aged 65 and older, does an eight-week balance class change the time a person can stand on one leg, compared with no class?
Null hypothesis: the mean change in single-leg stance time is the same in both groups. In symbols, mu one minus mu two equals zero.
Alternative hypothesis: the mean change differs between groups, in either direction. In symbols, mu one minus mu two is not zero.
Significance level: 0.05, two-sided, fixed before the data were examined.
The hypotheses are about the population of older adults like these participants, not about the 54 people in the sample.
Descriptive Results
Class group: 28 participants, mean change plus 6.8 seconds, standard deviation 6.0. Waiting group: 26 participants, mean change plus 1.2 seconds, standard deviation 5.4. Observed difference in mean change: 5.6 seconds.
Side-by-side box plots show both distributions roughly symmetric, with similar spread and no extreme outliers. One class participant improved by 21 seconds; she is included in every analysis.
The Test
Because the groups are independent and their standard deviations differ somewhat, the writeup applies the unequal-variance form of the t test, a choice that costs little even if the two spreads are in fact the same (Ruxton, 2006).
Standard error of the difference: add 36 over 28 and 29.16 over 26, then take the square root, which gives about 1.55 seconds. Test statistic: 5.6 divided by 1.55, or t equals 3.61. Degrees of freedom by the Welch formula: about 52. P value, two-sided: about 0.0007.
Decision: the p value is below the preset 0.05, so the null hypothesis of equal mean change is rejected.
The Answer in Seconds
The interval for the gap between the groups' mean changes, at 95 percent, is 5.6 plus or minus 2.007 times 1.55, or 2.5 to 8.7 seconds.
The class group improved by about 5.6 seconds more than the waiting group, and the data are compatible with a benefit anywhere from about 2.5 to 8.7 seconds, which is the sentence a program director needs, not the p value.
Whether 5.6 seconds matters is a clinical question. For older adults, single-leg stance is often reported in seconds up to a ceiling of 30, and a gain of several seconds is large relative to the waiting group's change. The writeup does not claim the class prevents falls, which this trial did not measure. Falls are the outcome older adults and their families care about most, and a trial large enough to detect fewer falls would need hundreds of participants followed for a year or more.
Assumptions and Limits
Independence: participants were randomized individually and attended separate classes, so their changes are treated as independent. Normality: with groups of 26 and 28 and roughly symmetric box plots, the t test is reasonable. Blinding: the assessor who timed the stance test did not know group assignment; participants did. Generalization: volunteers from senior centers may be fitter or more motivated than older adults in general, so the result applies most clearly to people like them (Bland, 2015).
Conclusion
The balance class produced a larger improvement in single-leg stance time than waiting, by about 5.6 seconds, with a 95 percent interval of 2.5 to 8.7. The evidence against no difference is strong, p below 0.001. Reporting the size and its interval, rather than the p value alone, follows current guidance on statistical reporting (Wasserstein & Lazar, 2016).
References
Bland, M. (2015). An introduction to medical statistics (4th ed.). Oxford University Press.
Ruxton, G. D. (2006). The unequal variance t-test is an underused alternative to Student's t-test and the Mann-Whitney U test. Behavioral Ecology, 17(4), 688-690. https://doi.org/10.1093/beheco/ark016
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
HS345 Unit 8 instructions, in plain terms
Unit 8 usually asks for a complete hypothesis test on a supplied dataset, written up in labeled parts: question, hypotheses in words and symbols, significance level, descriptive results, the test and its output, a decision, an interval and a conclusion in context. Writeups of about three pages are common. Marks follow hypotheses about the population fixed before the analysis, a test that fits the design, correct computation and a conclusion stated in the outcome's units, not only as reject or fail to reject. Assumptions should be checked by name with evidence. Cite a source for the chosen test and for reporting effect sizes alongside p values.
How the HS 345 Unit 8 example is put together
The writeup opens with the research question and hypotheses in words and symbols, a two-sided alternative and the significance level fixed in advance. Descriptive results give each group's size, mean change and standard deviation, with box plots described and one large improvement kept in. The test section justifies Welch's version of the t test, then shows the standard error, statistic, degrees of freedom, p value and decision. The answer section gives the difference with its interval; the highlighted sentence reports it as a size with uncertainty. A paragraph separates statistical from clinical meaning. Assumptions and limits are checked in turn, and the conclusion restates the result and its interval.
Where the points sit in the HS345 Unit 8 rubric
Writeups of a hypothesis test earn most of their marks for correct hypotheses and test choice, correct computation, and a conclusion in context. Hypotheses must concern the population and be set before the data are examined. Test choice is rewarded when it matches the design, such as independent groups. Computation covers the statistic, degrees of freedom and p value, with steps shown. The conclusion earns full credit when it gives an effect size with an interval in real units and says what the result does and does not show. Named assumption checks and clear labeled sections complete the expected structure. Box plots or another display of both groups make the descriptive section easier to judge and often earn a separate point.
Common HS345 Unit 8 mistakes, and how to avoid them
Many writeups state hypotheses about the sample means rather than the population. Picking a one-sided alternative only after the results show a direction is another frequent slip. Some students report only reject the null, with no effect size or interval. Others use a paired test for independent groups, or the reverse. Claiming a benefit the trial did not measure, such as fewer falls, overreaches. If your dataset has a different design, perhaps paired before and after measurements or three groups, share it and the hypotheses, test and conclusion can be set out in the same labeled order. Rounding the standard error too early can push the interval's limits off by a few tenths.
Get an HS345 Unit 8 example written to your instructions
Your HS345 Unit 8 writeup names its own groups and outcome, so the custom sample starts there. Supply the raw data or group means and standard deviations, plus the instructions and rubric. At no cost for the first order and back in 24-48 hours, it fixes hypotheses before calculating and gives the effect in whatever unit the outcome uses. 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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HS345 Unit 8 questions, answered
Why not use a one-sided test if the program was expected to help?
Because the direction has to be justified before the data are seen, and a program could plausibly harm some participants through fatigue or injury. A two-sided test is the more defensible default. Choosing a one-sided test after seeing that the program group improved effectively halves the p value, and graders treat that choice as a serious error.
Can the same structure handle a paired design?
Yes, with one change. If the same people were measured twice without a comparison group, the test becomes a paired t test on their differences, and the hypotheses concern the mean difference. The order of hypotheses, description, assumptions, results and interpretation stays the same, and a custom sample would follow the design your prompt describes.
Where can I find a free HS345 Unit 8 sample paper?
This page shows the complete Unit 8 writeup, testing whether a balance class changed single-leg stance time in older adults.
Why report a confidence interval after a hypothesis test?
The test says whether the data are compatible with no difference; the interval says how large the difference plausibly is, which is what a program director needs.
When should Welch's t test be used?
For two independent groups, especially when their spreads or sizes differ. It does not require equal variances and loses little when they happen to be equal.