Data Presentation in Research: Tables, Figures, and Text

Researchers face a constant challenge when preparing scientific manuscripts: deciding whether to present findings as tables, figures, or descriptive text. Each format serves a distinct purpose, and the choice affects how clearly readers understand the evidence, how easily they can verify the results, and how likely the work is to influence practice. This article compares the strengths and limitations of tables, figures, and text for presenting research data, provides practical guidelines for selecting the appropriate format, and outlines common errors that undermine effective communication of scientific findings.

At a Glance: Choosing the Right Presentation Format

The decision to use a table, figure, or text depends on the nature of the data, the message you need to convey, and the audience you are addressing. The following table summarizes the key characteristics of each format to help researchers make informed choices.

Understanding the Role of Each Format in Scientific Communication

Scientific papers communicate findings through a combination of tables, figures, and text. Each element carries part of the message, and the most effective manuscripts use all three formats strategically instead of duplicating the same information in multiple places.

Tables excel at presenting precise numerical values that readers may need to verify or use in calculations. They allow researchers to report many variables simultaneously without overwhelming the narrative. Figures, by contrast, communicate patterns and relationships that are difficult to discern from columns of numbers. A well-constructed figure can show a trend, a distribution, or a group difference at a glance. Text serves the essential function of interpretation, explaining what the data mean, how they relate to the research question, and why they matter.

The choice among these formats should follow from the research question and the nature of the data. A study examining the effect of a dietary intervention on metabolic markers might use a table to report baseline characteristics, figures to show changes over time, and text to highlight the main findings and their clinical implications. The goal is to present each piece of evidence in the format that communicates it most clearly and honestly.

Core Principles for Effective Data Presentation

Several fundamental principles guide the presentation of research data. These principles apply across disciplines and study designs, from laboratory experiments to clinical trials to qualitative inquiries.

Clarity and Accuracy Take Priority

The primary obligation of any data presentation is to represent the findings accurately. This means choosing formats that do not distort the underlying data and avoiding practices that mislead readers. For continuous data in small sample size studies, bar and line graphs can conceal the distribution of individual observations. Many different data distributions can produce the same bar or line graph, and the full data may suggest different conclusions from the summary statistics. Scatterplots, box plots, and histograms allow readers to critically evaluate continuous data and should be used when sample sizes are small enough that individual points matter.

Match the Format to the Message

The format should serve the message, not the reverse. If the key finding is that a treatment reduced blood pressure by a specific amount, a figure showing the change over time may be appropriate. If the key finding is the exact proportion of participants who experienced a particular outcome, a table with precise values may serve better. Researchers should ask what they want readers to take away from each element and choose the format that delivers that message most effectively.

Avoid Duplication Across Formats

Presenting the same data in a table and a figure wastes space and can confuse readers. Choose the format that communicates the information best and refer to it in the text without repeating all the values. The text should highlight the most important findings and direct readers to the table or figure for details.

Design for the Reader

Effective data presentation considers how readers will interact with the material. Tables should be organized logically with clear headings and consistent formatting. Figures should have legible labels, appropriate scales, and captions that explain what is shown. Both should be understandable without requiring the reader to search through the text for context.

Practical Workflow for Selecting Presentation Formats

Researchers can follow a systematic process when deciding how to present their data. This workflow helps ensure that each element serves a clear purpose and that the overall manuscript communicates effectively.

Step 1: Inventory Your Data

List all the data you need to present, including primary outcomes, secondary outcomes, baseline characteristics, and supplementary analyses. Note the type of each variable, the number of observations, and the key relationships you need to show.

Step 2: Identify the Key Messages

For each research question, identify the main finding you need to communicate. This might be a difference between groups, a change over time, a relationship between variables, or a distribution of responses. The key message determines the most appropriate format.

Step 3: Match Data to Formats

Assign each piece of data to the format that communicates it most clearly. Use tables for precise values and comparisons across many variables. Use figures for patterns, trends, and relationships. Use text for interpretation and for the few numbers that are central to the narrative.

Step 4: Design Each Element

Create each table and figure according to best practices. Ensure that tables are well organized and that figures are legible and accurately represent the data. Write captions that explain what each element shows without requiring the reader to consult the text.

Step 5: Review for Duplication and Gaps

Check that each piece of data appears in only one format and that all important findings are presented somewhere. Remove redundant elements and add any that are missing.

Step 6: Seek Feedback

Ask colleagues to review the tables and figures before submission. Ask them whether each element is clear, whether the formats are appropriate, and whether any data are missing or confusing.

Tables: Precision and Organization

Tables are the most efficient format for presenting exact values and comparing multiple variables. They are particularly useful for reporting baseline characteristics, statistical outputs, and detailed results that readers may need to verify or use in their own analyses.

When to Use Tables

Use tables when readers need precise numbers, when you have many variables to report, or when you need to compare values across groups. Tables are also appropriate for presenting data that do not have a natural visual representation, such as categorical variables or counts of events.

Designing Effective Tables

A well-designed table has a clear title, logical column headings, and consistent formatting. Units should be specified in the headings instead of repeated in every cell. Decimal places should be consistent within columns. Tables should be self-contained, with footnotes explaining abbreviations and statistical symbols.

Common Table Errors

Tables fail when they are too dense to read, when they duplicate information in figures, or when they present data in formats that obscure the message. A table with dozens of columns and rows may be comprehensive but unusable. Researchers should consider whether a figure would communicate the key pattern more effectively.

Figures: Visualizing Patterns and Relationships

Figures are the most powerful format for showing patterns, trends, and relationships in data. A well-constructed figure can communicate in seconds what would take paragraphs of text or a dense table to convey.

When to Use Figures

Use figures when the key message is a pattern or relationship instead of specific values. Figures are particularly effective for showing changes over time, dose-response relationships, distributions of continuous data, and comparisons between groups.

Choosing the Right Figure Type

The type of figure should match the nature of the data and the message. Scatterplots show relationships between two continuous variables. Box plots show distributions and identify outliers. Histograms show the shape of a distribution. Line graphs show changes over time. Bar graphs compare categorical groups.

For continuous data in small sample size studies, univariate scatterplots are often more informative than bar or line graphs because they show every data point. This allows readers to see the distribution and identify outliers or unusual patterns that summary statistics would conceal.

Designing Effective Figures

Effective figures have clear labels, appropriate scales, and captions that explain what is shown. Axes should be labeled with variable names and units. Scales should be chosen to show the data honestly without exaggerating or minimizing differences. Colors and symbols should be accessible to readers with color vision deficiencies.

Common Figure Errors

Figures fail when they distort the data, when they are too complex to interpret, or when they present summary statistics without showing the underlying distribution. Bar and line graphs that show only means and error bars can mislead readers when the data are not normally distributed or when sample sizes are small. Figures should always represent the data accurately and completely.

Text: Interpretation and Emphasis

Text serves the essential function of interpretation in a research paper. It explains what the data mean, how they relate to the research question, and why they matter. Text also carries the findings that are most important to the narrative.

When to Use Text

Use text for the main findings, for interpretation, and for data that are central to the argument. Text is also appropriate for qualitative findings, where themes and patterns are better described in words than in tables or figures.

Integrating Text with Tables and Figures

The text should direct readers to tables and figures and highlight the most important findings without repeating all the values. A common pattern is to state the finding, provide the key numbers, and refer readers to the table or figure for complete data. For example, a sentence might state that the treatment group showed a significant reduction in blood pressure compared with the control group, with the full data presented in a figure.

Common Text Errors

Text fails when it repeats all the values from tables and figures, when it presents too many numbers without context, or when it interprets data in ways that are not supported by the evidence. Researchers should use text to emphasize the findings that matter most and to explain their significance.

Comparing Presentation Formats: Evidence from Published Research

Research on data presentation practices provides insight into common problems and effective solutions. Studies of published papers reveal persistent issues with how data are presented and reported.

Persistent Problems in Statistical Reporting

A cross-sectional analysis of research papers published in two pharmacology and physiology journals examined whether reporting practices improved after editorial advice was published. The analysis found no evidence that reporting practices changed following the editorial advice. Most papers used standard errors of the mean to summarize data variability, most did not report exact p-values for primary analyses and post-hoc tests, and very few plotted raw data used to calculate variability. Papers that reported p-values between 0.05 and 0.1 often interpreted these as trends or statistically significant. These findings suggest that editorial advice alone may not be sufficient to improve reporting practices and that stronger incentives or enforcement may be needed.

The Problem with Bar and Line Graphs

A systematic review of research articles published in top physiology journals found that papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic because many different data distributions can lead to the same bar or line graph, and the full data may suggest different conclusions from the summary statistics. The authors recommended training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies.

Data Presentation Format Affects Interpretation

Research on pore size distribution data from shale samples demonstrated that the format of data presentation can affect the interpretation of results. Three different presentations of pore size distributions were calculated from nitrogen adsorption and mercury intrusion data. The comparison showed that different presentations from the same sample could demonstrate different types of important pore information. One format depended largely on experimental data spacing, another amplified the role of smaller pore ranges, and a third represented the importance of larger pore ranges. The heterogeneity index calculated from one format was much larger than the index from another format. The authors concluded that different formats are more suitable for different measurement methods.

The Role of Supplementary Information

Supplementary information can enhance the transparency, credibility, and reproducibility of research when used effectively. However, supplementary materials often suffer from inconsistent formatting, poor accessibility, and fragmented organization that impede instead of advance understanding. Recommendations for effective supplementary information include treating it with the same care as the main text, using it strategically to support the scientific narrative, creating a single well-structured self-contained master document, ensuring explicit cross-referencing between the main text and supplementary information, and avoiding the misuse of supplementary information as a substitute for proper data repositories.

Quality Controls for Data Presentation

Several quality control measures can help researchers ensure that their data presentation meets professional standards. These measures apply at different stages of the research and publication process.

Journal Guidelines and Reporting Standards

Most journals provide specific instructions for authors regarding data presentation. These guidelines typically cover figure formats, table styles, statistical reporting, and supplementary materials. Researchers should consult the journal's instructions early in the writing process and follow them carefully. Reporting standards and checklists are available for many study types and can help ensure that all essential elements are included.

Statistical Reporting Standards

Statistical reporting should follow established standards for the study type. This includes reporting exact p-values instead of ranges, specifying measures of variability, and describing the statistical methods used. Researchers should avoid practices that have been identified as problematic, such as using standard errors of the mean when standard deviations are more appropriate or interpreting non-significant results as trends.

Peer Review and External Feedback

Peer review provides an important quality control mechanism for data presentation. Reviewers can identify figures that are misleading, tables that are too dense, or text that overinterprets the data. Researchers should take reviewer comments seriously and revise their presentations accordingly. Seeking feedback from colleagues before submission can also help identify problems early.

Data Sharing and Reproducibility

Making data available to other researchers supports reproducibility and allows readers to verify the findings presented in tables and figures. Data repositories provide a more appropriate venue for raw data than supplementary materials attached to a paper. Researchers should deposit their data in recognized repositories and provide clear documentation of the data and analysis methods.

Common Failure Patterns in Data Presentation

Understanding common failure patterns can help researchers avoid errors in their own work. These patterns appear across disciplines and study types.

Presenting Continuous Data as Bar Graphs

Bar graphs are widely used to present continuous data, but they are often inappropriate for this purpose. Bar graphs show only summary statistics, such as means and error bars, and conceal the distribution of individual observations. When sample sizes are small, readers cannot determine whether the data are normally distributed, whether there are outliers, or whether the summary statistics accurately represent the data. Scatterplots, box plots, and histograms provide more complete information.

Using Inconsistent or Misleading Scales

Figures can mislead readers when scales are chosen to exaggerate or minimize differences. Truncating the y-axis can make small differences appear large, while expanding the scale can make important differences appear trivial. Researchers should choose scales that represent the data honestly and should clearly indicate any breaks or transformations in the axis.

Overloading Tables with Data

Tables that include every variable and every comparison become difficult to read and may obscure the key findings. Researchers should include only the data that are essential to the message and should consider whether some data belong in supplementary materials instead of the main text.

Duplicating Data Across Formats

Presenting the same data in a table and a figure wastes space and can confuse readers. Researchers should choose the format that communicates the information best and should not repeat the same values in multiple places.

Interpreting Results Beyond the Evidence

Data presentation should be distinguished from interpretation. Researchers should present the data accurately and then interpret them appropriately, without overstating the findings or drawing conclusions that are not supported by the evidence. The analysis of published papers found that implied or gross spin was noted in papers before and after editorial advice was published, indicating that this problem persists.

Poorly Organized Supplementary Materials

Supplementary materials that are poorly organized, inconsistently formatted, or difficult to navigate undermine the transparency and reproducibility of research. Researchers should treat supplementary information with the same care as the main text and should follow best practices for organization and cross-referencing.

Limitations and Considerations Across Research Approaches

Different research approaches present different challenges for data presentation. Researchers should be aware of the specific considerations for their study type.

Quantitative Research

Quantitative research relies heavily on tables and figures to present numerical data. Researchers must ensure that statistical reporting is complete and accurate, that figures represent the data honestly, and that tables are organized for easy comprehension. The choice of summary statistics and the presentation of variability are particularly important considerations.

Qualitative Research

Qualitative research presents data through narrative descriptions, quotes, and thematic summaries. Tables and figures can be used to organize and display qualitative findings, such as themes, categories, and relationships between concepts. The presentation should maintain the richness and context of the data while providing sufficient structure for readers to understand the findings.

Mixed Methods Research

Mixed methods research combines quantitative and qualitative approaches and may present both types of data. Researchers should consider how to integrate the different types of evidence and how to present them in a way that shows the relationships between the quantitative and qualitative findings.

Integrative Reviews

Integrative reviews synthesize literature to guide practice and face unique challenges in data presentation. A review of 151 integrative reviews published in nursing journals found that most reviews used narrative descriptions for presentations of findings, but some reviews also used innovative tables, concept maps, frameworks, and word clouds to enhance data presentation. The analysis and synthesis methods varied, with many reviews not explicitly delineating their methods.

Records and Documentation for Data Presentation

Maintaining thorough records of data analysis and presentation decisions supports transparency and reproducibility. Researchers should document the following elements.

Data Analysis Documentation

Record the statistical methods used, the software and versions, and the specific analyses performed. This documentation should be detailed enough that another researcher could reproduce the analysis from the raw data.

Figure and Table Creation Records

Document how each figure and table was created, including the data used, the software, and any transformations or calculations applied. This information is important for verifying the accuracy of the presentation and for responding to reviewer questions.

Version Control

Maintain version control for manuscripts and data files to track changes over time. This is particularly important when multiple authors are involved or when revisions are requested during peer review.

Data Repository Deposits

Deposit raw data in recognized repositories and document the deposit with persistent identifiers. This supports reproducibility and allows readers to verify the findings presented in the paper.

Professional Escalation Criteria

Researchers should seek additional guidance or escalate concerns in certain situations. The following circumstances warrant consultation with senior colleagues, statisticians, or other experts.

Uncertainty About Statistical Methods

If you are uncertain about the appropriate statistical methods for your data or the correct way to present statistical results, consult a statistician or a senior colleague with relevant expertise. Incorrect statistical reporting can undermine the credibility of otherwise sound research.

Complex Data Structures

Data with complex structures, such as hierarchical or longitudinal data, may require specialized presentation approaches. Consult experts who have experience with similar data types.

Journal-Specific Requirements

If you are uncertain about a journal's requirements for data presentation, contact the editorial office for clarification. Following the journal's guidelines is essential for successful publication.

Concerns About Data Integrity

If you have concerns about the integrity of the data or the analysis, escalate these concerns to your institution's research integrity office or equivalent body. Research integrity involves honesty, transparency, and respect for ethical standards throughout all stages of the research endeavor.

Safety and Regulatory Context

Data presentation in research is governed by ethical and regulatory requirements that vary by jurisdiction and funding source. Researchers should be aware of the requirements that apply to their work.

Research Integrity Standards

Research integrity refers to the moral and ethical standards that serve as the foundation for research activities. Integrity in research incorporates principles of honesty, transparency, and respect for ethical standards throughout all stages of the research endeavor, encompassing study design, data collection, analysis, reporting, and publishing. Researchers, institutions, journals, and readers share responsibilities for preserving research integrity.

Reporting Guidelines

Many journals require adherence to reporting guidelines specific to study type. These guidelines ensure that essential elements are reported and that data are presented in a standardized format. Researchers should consult the appropriate guidelines for their study design.

Data Protection and Privacy

Research involving human participants must comply with data protection and privacy requirements. Data presentation should not compromise participant confidentiality, and identifying information should be removed or anonymized before publication.

Funding Agency Requirements

Funding agencies may have specific requirements for data management, sharing, and presentation. Researchers should review their funding agreements and comply with all applicable requirements.

Frequently Asked Questions

What is the best way to present continuous data in a research paper?

For continuous data, particularly in small sample size studies, scatterplots, box plots, and histograms are generally more informative than bar and line graphs. These formats show the distribution of individual observations and allow readers to evaluate the data critically. Bar and line graphs can conceal the distribution of the data, and many different data distributions can produce the same bar or line graph.

When should I use a table instead of a figure?

Use a table when readers need precise numerical values, when you have many variables to report, or when you need to compare values across groups. Use a figure when the key message is a pattern, trend, or relationship that is better communicated visually. Avoid presenting the same data in both a table and a figure.

How can I avoid misleading readers with my figures?

Choose scales that represent the data honestly, show individual data points when sample sizes are small, and avoid practices that exaggerate or minimize differences. Ensure that all axes are labeled with variable names and units, and provide captions that explain what is shown.

What are the most common data presentation errors in published papers?

Common errors include presenting continuous data as bar graphs without showing the underlying distribution, using standard errors of the mean when standard deviations are more appropriate, failing to report exact p-values, interpreting non-significant results as trends, and duplicating data across tables and figures.

How should I handle supplementary materials?

Treat supplementary information with the same care as the main text. Create a single, well-structured, self-contained supplementary document, ensure explicit cross-referencing between the main text and supplementary information, and avoid using supplementary materials as a substitute for proper data repositories.

What should I do if I am uncertain about how to present my data?

Consult the journal's instructions for authors, seek advice from senior colleagues or statisticians, and consider using reporting guidelines specific to your study type. Getting feedback from colleagues before submission can help identify problems early.

How does data presentation affect the interpretation of research findings?

The format of data presentation can affect how readers interpret the findings. Different presentations of the same data can emphasize different aspects, such as smaller versus larger ranges or different patterns of variability. Researchers should choose formats that represent the data accurately and communicate the intended message.

What role does data presentation play in research integrity?

Accurate data presentation is a component of research integrity, which involves honesty, transparency, and respect for ethical standards throughout the research process. Misleading data presentation can constitute spin or scientific misconduct and undermines the credibility of research. Researchers have a responsibility to present their data accurately and completely.

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• Scientific Data Provenance For Reproducible Research

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• NCBI Literature Resources. National Center for Biotechnology Information.
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• A Review of the Quality Indicators of Rigor in Qualitative Research.. American journal of pharmaceutical education, 2020.
• Interpretive description: A flexible qualitative methodology for medical education research.. Medical education, 2021.
• Research Integrity: Where We Are and Where We Are Heading.. Journal of Korean medical science, 2023.
• Guidance on how to develop complex interventions to improve health and healthcare.. BMJ open, 2019.
• Undertaking a scoping review: A practical guide for nursing and midwifery students, clinicians, researchers, and academics.. Journal of advanced nursing, 2021.
• False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant.. Psychological science, 2011.
• Data Analysis and Presentation in Integrative Reviews: A Narrative Review.. Western journal of nursing research, 2022.
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• Ten simple rules for making the supplement increase your paper's impact.. 2026.
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• Research on Accounting Treatment and Information Presentation of Enterprise Data Assets. E3s Web of Conferences, 2021.
• Research Methods: Data Presentation Effects on Quality Management Decision-Making. 30th Americas Conference on Information Systems Amcis 2024, 2024.

This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.