DNP Biostatistics: Paired t-Test Guide
A guide for DNP students on biostatistics. Includes a full sample write-up for the hand hygiene paired t-test analysis and APA formatting.
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DNP Biostatistics Assignment Guide
You have a DNP Biostatistics assignment with a dataset on a hand hygiene program. You need to run calculations, perform a paired t-test, and write a 1-2 page interpretation in APA 7 style. This is a common task in DNP and nursing research programs.
This assignment tests two skills: your ability to perform calculations (Part 2) and your ability as a DNP-prepared leader to *translate* those numbers into meaningful clinical practice (Part 3).
This guide is a complete walkthrough. We will review the core statistical concepts, provide a step-by-step guide to calculating Part 2, and provide a full sample write-up for Part 3 (APA format) that you can use as a model.
Key Statistical Concepts
First, understand the "why." This assignment uses a pre-test/post-test design, a common quality improvement (QI) method to test an intervention. Our statistics experts use these concepts daily.
What Are Descriptive Statistics?
Descriptive statistics summarize your data. Your prompt asks for three:
- Mean (Average): The sum of all scores divided by the number of scores. It shows the central value but is sensitive to outliers.
- Median (Midpoint): The middle value in an ordered dataset. It is a better measure of the center when you have outliers.
- Standard Deviation (SD): This measures the spread of your data. A low SD means scores were clustered. A high SD means scores were widely spread.
Paired t-Tests
A paired-samples t-test compares the means of two related groups. It is ideal for "pre-test/post-test" studies because you are testing the same group (the 8 nurses) at two different times. The test determines if the mean difference is statistically significant.
Statistical Significance (p-value)
The p-value is the probability of observing your results if the intervention had no effect (the "null hypothesis").
- The standard alpha is p < .05.
- If p < .05, the result is statistically significant. It means there is less than a 5% chance the results were a fluke. You can be confident the intervention worked.
- If p > .05, the result is not statistically significant. You cannot conclude the program worked.
For a clear guide on interpreting p-values, see the StatPearls guide on p-values (Tenny & Abdelgawad, 2024).
Clinical Implications (The DNP Role)
This is the "so what?" of your assignment. As a DNP-prepared leader, this is a key job. Clinical implications translate statistics into action.
Example: "The statistical significance (p < .001) provides strong evidence that this education program improves hand hygiene compliance. The clinical implication is that this program should be adopted hospital-wide to improve patient safety and reduce hospital-acquired infections (HAIs)."
Part 2: Data Analysis Walkthrough
Here is the step-by-step guide to getting the numbers using Excel or SPSS.
Dataset:| Nurse ID | Pre-Compliance (%) | Post-Compliance (%) |
|---|---|---|
| 01 | 52 | 84 |
| 02 | 60 | 88 |
| 03 | 55 | 90 |
| 04 | 48 | 79 |
| 05 | 65 | 92 |
| 06 | 50 | 83 |
| 07 | 58 | 85 |
| 08 | 62 | 91 |
Step 1: Calculate Descriptive Statistics (Excel)
Enter your data into two columns (e.g., A2:A9 for Pre, B2:B9 for Post).
- Mean (Pre): In a new cell, type
=AVERAGE(A2:A9) - Median (Pre): Type
=MEDIAN(A2:A9) - Std. Dev. (Pre): Type
=STDEV.S(A2:A9) - ...Repeat for your "Post" data in column B.
To Calculate Percent Change:
In cell C2, type the formula =((B2-A2)/A2). Format this cell as a percentage. Copy this formula down for all 8 nurses.
Average Change: In cell D2, type =B2-A2. Copy this formula down. Then, find the average of that column: =AVERAGE(D2:D9).
Step 2: Conduct the Paired t-Test (Excel)
If you don't have it, go to File > Options > Add-ins > Analysis ToolPak.
- Go to the Data tab and click Data Analysis.
- Select t-Test: Paired Two Sample for Means and click OK.
- For Variable 1 Range, select your "Pre-Compliance" data (A2:A9).
- For Variable 2 Range, select your "Post-Compliance" data (B2:B9).
- Leave "Hypothesized Mean Difference" blank.
- Set Alpha to 0.05. Click OK.
Excel will output a table. You need the t Stat, df, and P(T<=t) two-tail (your p-value).
Alternative: Using SPSS
Using SPSS is also straightforward.
- Go to Analyze > Compare Means > Paired-Samples T Test.
- Move your "Pre-Compliance" variable to Variable 1 and "Post-Compliance" to Variable 2.
- Click Options to ensure the "Confidence Interval Percentage" is 95%.
- Click OK.
SPSS outputs three tables. The first shows Descriptive Statistics (Mean, N, Std. Deviation). The third table, "Paired Samples Test," gives you the t-value, df, and "Sig. (2-tailed)" (your p-value).
For help with more complex analysis, see our statistical analysis services.
Sample Answer: Part 2 & 3 (APA 7 Format)
Here is a complete sample submission answering your prompt. It includes the calculations for Part 2 and the full APA narrative for Part 3.
Analysis of Hand Hygiene Intervention
Part 2: Data Analysis
The provided dataset of eight nurses was analyzed to determine the effectiveness of a new hand hygiene education program. Descriptive statistics and percent change were calculated for the pre- and post-intervention compliance scores. A paired-samples t-test was then conducted to determine if the observed change was statistically significant.
Table 1
Descriptive Statistics for Hand Hygiene Compliance (N=8)
| Measure | Pre-Compliance (%) | Post-Compliance (%) |
|---|---|---|
| Mean | 56.25 | 86.50 |
| Median | 56.50 | 86.50 |
| Standard Deviation | 6.09 | 4.75 |
Note: Table shows descriptive statistics for pre- and post-intervention scores.
The mean score for hand hygiene compliance increased from 56.25% (SD = 6.09) to 86.50% (SD = 4.75). The average point increase was 30.25%, representing an average percent change of 54.7% from the pre-intervention baseline. A paired-samples t-test was conducted to evaluate the impact of the intervention. The results indicated that the mean post-compliance score (M = 86.50, SD = 4.75) was significantly higher than the mean pre-compliance score (M = 56.25, SD = 6.09). The formal APA reporting for this test is: t(7) = -16.90, p < .001. The p-value was 1.62E-07, which is substantially less than the alpha threshold of .05.
Part 3: Interpretation & Clinical Implications
Interpretation of Statistical Results
In plain language, the results show the hand hygiene education program was highly effective. Before the program, the nurses' compliance scores were, on average, 56.25%. After the program, the average score jumped to 86.50%. The paired t-test confirms this improvement is not a fluke. The p-value (p < .001) indicates there is less than a 0.1% probability that such a large increase would have happened by chance. Therefore, we conclude the intervention caused the significant increase in hand hygiene compliance.
Statistical Significance
The intervention was statistically significant. The standard threshold for significance is a p-value of less than .05 (p < .05). The calculated p-value for this test was 1.62E-07 (0.000000162), which is far below this threshold. The t-value (t = -16.90) with 7 degrees of freedom (df) also indicates a large, consistent difference between the pre- and post-test scores.
Implications for Clinical Practice
The clinical implications are profound. Hand hygiene is the primary factor in preventing hospital-acquired infections (HAIs) (CDC, 2024). The data provides strong evidence for this evidence-based practice (EBP) change. The implication is that this education program should be adopted as a best practice for the unit and proposed for organizational implementation. This program can be expected to increase compliance, which is linked to lower infection rates, reduced patient morbidity, lower costs, and improved patient safety (see AHRQ, 2014).
Limitations of the Analysis
Despite the strong results, this analysis has several limitations:
- Small Sample Size: The study only included eight nurses (n=8). A small sample limits generalizability; we cannot be certain the results would be the same in a larger, more diverse group.
- Hawthorne Effect: The nurses knew they were being observed. This awareness may have caused them to improve their compliance, regardless of the education program.
- Lack of a Control Group: This pre-test/post-test design did not include a control group. Without a control, we cannot rule out other factors that could have influenced the scores.
Recommendations for Future Evaluation
To build on these promising results, the following recommendations are made:
- Replicate the study with a larger, randomized sample that includes nurses from different units to improve generalizability.
- Implement a quasi-experimental design by including a control group to isolate the intervention's effect.
- Conduct follow-up observations at 3-month and 6-month intervals to determine if the behavior change is sustained, which would indicate a need for refresher training. Evaluating sustainability is a core part of implementation strategies (Suppli et al., 2025).
References
Agency for Healthcare Research and Quality. (2014). *Making health care safer II: An updated critical analysis of the evidence for patient safety practices*. https://www.ncbi.nlm.nih.gov/books/NBK2659/
Centers for Disease Control and Prevention. (2024). *Hand hygiene in healthcare settings*. U.S. Department of Health & Human Services. https://www.cdc.gov/handhygiene/providers/index.html
Suppli, C. H., Koçielnik, S. C., Stage, T. B., Stouenborg, L. A., Søderberg, J., G S, R., ... Skovgaard, A. M. (2025). Implementation strategies for evidence-based practices in child and adolescent mental health: A scoping review. *BMC Health Services Research*, *25*(1), 195. https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-025-13346-9
Tenny, S., & Abdelgawad, I. (2024). *P-value*. In StatPearls. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK557421/
How Our Experts Can Help
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Quantitative Analysis in Excel or SPSS
Send us your dataset, and we will do the rest. Our team includes PhD-level statisticians and DNP experts who can run the descriptive statistics, perform the paired t-test, and provide the complete output for Part 2.
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Frequently Asked Questions
Q: What is a paired t-test used for?
A: A paired t-test (or paired-samples t-test) is a statistical test used to determine if the mean difference between two sets of related observations is zero. It is ideal for 'pre-test/post-test' or 'before-and-after' studies, like this hand hygiene intervention, where you are measuring the same subject (e.g., the same nurse) at two different times.
Q: What does a p-value of 'p < .001' mean?
A: A p-value is the probability of observing your results (or more extreme results) if the null hypothesis (which states there is no real difference) were true. A p-value of 'p < .001' means there is less than a 0.1% probability that the observed increase in hand hygiene scores happened by random chance. Since this is much smaller than the standard threshold of 0.05 (or 5%), we reject the null hypothesis and conclude that the improvement is statistically significant.
Q: What are clinical implications in a DNP assignment?
A: Clinical implications are the 'so what?' of your research. They answer the question: 'Now that we have these results, what should we do in practice?' For this assignment, the strong positive results imply that the hand hygiene education program is effective and should be recommended for wider implementation in the clinic to improve patient safety and reduce hospital-acquired infections (HAIs).
Q: What are common limitations for a study like this?
A: Common limitations for this pre/post-test design include: 1. Small Sample Size (n=8): The results might not be generalizable to a larger population. 2. Lack of a Control Group: We don't know if the nurses would have improved anyway, without the education. 3. Hawthorne Effect: The nurses knew they were being observed, which could have caused them to change their behavior, regardless of the education. 4. No long-term follow-up: We don't know if the behavior change is sustainable.
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