Statistical and Visual Reporting

Statistics are part of the scientific argument, not a decorative language added after drafting. The writer must understand enough to report the analysis faithfully, detect mismatches, and ask the statistician precise questions.

Start with the design and estimand-like question

Before interpreting a number, ask what was compared, in whom, over what period, using which outcome definition, and under which handling of intercurrent events, missing data, and analysis population. The same numerical difference can answer different clinical questions under different analysis choices.

Methods are the document's Rosetta stone: they define how the Results can be read. A statistically polished sentence cannot compensate for an unclear population, endpoint, model, or time point.

Describe the data before testing it

Use statistics that fit the variable and distribution. Means and standard deviations describe different features from medians and ranges or interquartile ranges. Counts need denominators. Percentages need clarity about the population, missing observations, and whether categories overlap. Avoid excessive decimal places that imply unsupported precision.

Report units consistently. Explain transformations, derived endpoints, category thresholds, and composite outcomes. A table footnote should resolve interpretation, not hide essential methods.

Report estimates and uncertainty

A p-value does not show the size, direction, precision, or clinical relevance of an effect. Pair inferential results with the effect estimate and an interval measure where appropriate. State the exact p-value unless a justified threshold convention applies, and avoid “p = 0.000.” Distinguish a failure to reject a null hypothesis from proof of equivalence or absence of effect.

Interpret confidence intervals in context. Wide intervals may include meaningfully different conclusions. Statistical significance can coexist with trivial clinical effect, and a clinically important estimate can be imprecise. Keep statistical and clinical interpretation connected but distinct.

Name the analysis honestly

Identify models and tests, variables included, adjustment factors, interaction terms, repeated-measure handling, multiplicity approaches, missing-data methods, sensitivity analyses, and software when relevant. Separate prespecified, supportive, exploratory, and post-hoc analyses. If an analysis changed after seeing the data, the prose should not imply prospective planning.

In observational work, adjustment does not guarantee removal of confounding. In subgroup work, a significant result in one group and a nonsignificant result in another does not itself establish interaction. In time-to-event work, define the event, censoring, analysis time, and measures presented. In diagnostic work, provide the reference standard and the denominators behind sensitivity and specificity.

Make harms visible

Report the safety population, observation period, coding and collection approach, severity and seriousness, treatment relationship when assessed, withdrawals, deaths, and events of special interest as applicable. Use consistent denominators. Avoid comparing raw percentages without attention to exposure or study design. Keep zero-event statements within the observed sample and period.

Tables and figures must answer reader questions

Choose the display from the comparison or pattern the reader needs to see.

• Use a table when exact values and multidimensional lookup matter.
• Use a figure when pattern, distribution, change, relationship, or flow matters.
• Use a flow diagram to explain movement through a process or study.
• Use a forest plot to show effects and uncertainty across analyses or studies.
• Use a survival plot only with clear risk sets, time origin, censoring context, and interpretable groups.

Every display needs a purpose, standalone title or legend, defined abbreviations, units, analysis population, denominators where needed, and traceable source. Axes and scales must not create a false impression. Color must carry meaning consistently and not be the sole way to distinguish critical information.

Statistical QC pass

Check:

• Methods name every analysis needed to understand the reported results;
• populations, endpoints, time points, models, and estimands or comparison questions align;
• counts, denominators, percentages, effect estimates, intervals, and p-values match the validated source;
• precision and rounding are consistent and do not change interpretation;
• text, tables, figures, abstract, and conclusion agree;
• subgroup, sensitivity, exploratory, and post-hoc analyses are labeled;
• limitations reflect design, conduct, missing data, multiplicity, power, bias, and generalizability where relevant;
• the narrative does not convert association into causation or nonsignificance into equivalence;
• every visual can be interpreted without deceptive scale, missing labels, or hidden denominator changes.