When a midwifery researcher finishes collecting data on maternal mortality, partograph use, or exclusive breastfeeding rates, the raw numbers tell no immediate story. Presenting data transforms a chaotic spreadsheet into clear evidence that can improve clinical practice across Nigerian health facilities. Midwives must learn to organize findings logically so that readers, examiners, and policy-makers grasp the core message without wading through endless calculations. A good data presentation answers specific research questions directly, highlights key trends, and lays the groundwork for the discussion chapter. Whether reporting quantitative survey results from antenatal clinics or clinical audit outcomes from the labor ward, the goal is clarity, honesty, and professional rigor.
Tables are the backbone of quantitative results presentation. A well-designed table allows readers to scan exact figures quickly without reading dense paragraphs.
While tables excel at showing exact figures, charts and graphs capture visual attention and reveal overall patterns instantly. Choosing the right chart depends entirely on the nature of the data you collected.
Selecting the wrong statistical test is a frequent error in student research. The choice of test is governed by your research question, study design, and the type of data you collected.
| Data Type / Goal | Parametric Test (Normal Distribution) | Non-Parametric Test (Skewed Distribution) |
|---|---|---|
| Compare two independent groups | Independent Samples t-test | Mann-Whitney U test |
| Compare two paired groups | Paired t-test | Wilcoxon Signed-Rank test |
| Assess relationship between categories | Chi-Square test of independence | Chi-Square test of independence |
Always check whether your continuous data follows a normal bell curve before deciding between parametric and non-parametric options.
Numbers generated by statistical software require careful interpretation within the clinical context of midwifery. A p-value tells you the probability that an observed difference or association happened purely by chance. In most medical and nursing research, a threshold of p < 0.05 is accepted as statistically significant, meaning there is less than a 5% probability that the result is a fluke.
However, statistical significance does not automatically mean clinical importance. A very large sample size can produce a tiny p-value for a trivial clinical difference. This is why confidence intervals (CI), usually set at 95%, are vital. A confidence interval gives a range of values that likely contains the true population effect, showing both the precision and the practical bounds of your research findings.
Even with solid data, student researchers often make avoidable errors during the presentation phase. One frequent mistake is repeating every single table number directly in the text; narrative text should highlight key highlights and trends rather than reciting every cell. Another trap is drawing causal conclusions from purely correlational data—finding a statistical link between two variables does not prove one caused the other. Finally, students sometimes present confusing graphs with cluttered labels, missing axes, or misleading 3D effects that distort the visual proportions of the data. Keeping tables clean, staying objective in the text, and focusing on the clinical story prevents these errors.