How to Present and Visualise Research Data in a Paper (2026)

How you present and visualise data in a research paper directly affects how clearly your findings communicate — and how seriously they’re received. Raw numbers buried in paragraph form are hard to process. Well-chosen visuals reveal patterns that text simply cannot convey as efficiently. This guide covers which visualisation to use when, how to format it correctly for APA 7th, and how to integrate data presentation into your narrative.

Choosing the Right Visualisation for Your Data

The choice of chart or table type should be driven by what you’re trying to show, not by what looks impressive:

Pie charts are generally avoided in academic writing because they make proportional comparisons difficult — especially once you have more than four or five categories. Stacked bar charts convey the same information more accurately. In our experience reviewing Indian PhD theses, pie charts tend to survive mainly in early drafts; experienced examiners notice.

Tables: When to Use and How to Format

Use a table when readers need to look up specific values, compare multiple items across attributes, or when exact numbers matter more than the visual trend.

APA 7th Table Formatting Rules

• Number tables sequentially (Table 1, Table 2) and reference each in the text before it appears
• Title goes above the table: bold, title case
• Horizontal lines only — at the top, below column headers, and at the bottom. No vertical lines, no internal horizontal dividers
• Note section below the table for abbreviations, statistical symbols, and copyright attribution

Figures: When to Use and How to Format

Use a figure when the visual pattern, trend, or relationship is the point — when a reader should see it, not just read numbers about it.

APA 7th Figure Formatting Rules

• Number figures sequentially (Figure 1, Figure 2) and reference each before it appears
• Title goes below the figure: bold, title case — the opposite placement from tables
• Label both axes with the variable name and unit of measurement
• Legend must explain all symbols, colours, and patterns used
• Error bars are expected for experimental data; specify what they represent (SD, SE, or 95% CI) in the figure note

Integrating Visuals Into Your Narrative

Every table and figure must be discussed in the text. Never let a visual stand without explanation. “Table 1 shows the results” is not an explanation — it’s an index entry. Tell readers what the key finding is and what it means:

Weak: “Table 2 presents the comparison of accuracy scores across the three conditions.”

Strong: “Tool B achieved significantly higher accuracy than Tool A across all three conditions (Table 2), with the largest difference observed in the paraphrase-detection task (83% vs 61%, p < .001).”

The text directs readers to the key finding; the table or figure provides the supporting detail. (This is the section most PhD viva panels will quietly judge you on, by the way.)

Accessible and Reproducible Data Presentation

Colour Use

Design figures to be readable in greyscale. Colour may be unavailable in print or inaccessible to colour-blind readers — and in many Indian journals, figures still go to press in black and white. Use pattern fills, different line styles (solid, dashed, dotted), or shapes to differentiate data series independently of colour.

Data Transparency

Open science norms increasingly expect underlying data to be available, not just the summary visualisations. Check whether your target journal requires a data availability statement. For Indian PhD theses, attaching raw data as an appendix is still the norm rather than depositing it in a repository — but that is changing, and some universities now ask for it explicitly.

Descriptive Statistics Before Inferential Statistics

Before reporting significance tests, report descriptive statistics for each group: means, standard deviations, sample sizes. Readers need to understand what the data looks like before they can evaluate whether the differences are statistically significant.

Common Visualisation Mistakes

• Y-axis not starting at zero: Truncating the y-axis on a bar chart visually exaggerates differences. Start at zero for bar charts — though line charts may appropriately zoom in on a narrower range.
• 3D charts: 3D effects distort proportions and make reading exact values impossible. Use 2D only.
• Unlabelled axes: A figure without labelled axes is uninterpretable. Full stop.
• Duplicating data: Presenting the same data in both a table and a figure is redundant. Choose whichever format communicates the finding more effectively.