Summarise data
Describe hundreds of patient records with just a few numbers.
Used everywhere
DMFT scores, pocket depths, plaque indices — all rely on descriptive stats.
Report results
Every dental research paper starts with a descriptive statistics table.
Use this when the values are roughly symmetric and there are no major outliers. Good examples are age, plaque index, or bone loss scores that cluster evenly around the center.
Choose this when the data are skewed, long-tailed, or pulled by a few very large values. Pocket depth, treatment cost, and DMFT often behave this way.
Best for categories and repeated scores. If you want to describe the most common tooth type, diagnosis, or patient response, mode and percentages are usually the clearest.
Range is easy to understand but too sensitive to extremes to stand alone. It works best as a quick preview of spread, not as the only way to report variability.
Click a card to learn and try it.
How consistent or varied is your data?
Watch how a researcher analyses 5 patients step-by-step.
Normal data
Mean ± SD
e.g. Age: 34.5 ± 8.2 yrs
Skewed data
Median (IQR)
e.g. Pocket depth: 4 (3–6) mm
Categories
Frequency (%)
e.g. Smokers: 42 (34%)
| Variable | Mean ± SD | Median (IQR) |
|---|---|---|
| Age (years) | 34.5 ± 8.2 | — |
| Pocket Depth (mm) | — | 4 (3–6) |
| Plaque Index | 1.7 ± 0.5 | — |
Often right-skewed because many patients have low scores while a smaller group has very high disease burden. Median and IQR are often safer than mean alone.
Can contain extreme values from severe periodontitis. Show spread clearly so readers see whether the sample is tightly clustered or highly variable.
These are commonly summarized by mean +/- SD when the distribution is fairly balanced, especially in intervention studies with repeated measurements.
Variables like smoking, diabetes status, or implant success should be shown as counts and percentages so the group composition is easy to compare.