Designing Effective Figures for Different Types of Data

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-27

Quick Answer

• Match the visualization to the data type: continuous data suits scatter plots and histograms, categorical data suits bar charts, and time-series data suits line charts.
• Assess your data structure first, then select a chart that preserves the patterns you need to communicate to your audience.
• No single chart type works for every dataset, and poor chart selection can hide outliers, trends, or group differences.

Understanding Data Types and Visualization Goals

Effective figure design begins with a clear understanding of the data structure you are working with. Researchers often default to familiar chart types without considering whether those charts actually reveal the patterns present in their data. This mismatch between data type and visualization can obscure meaningful relationships, mislead readers, and weaken the impact of otherwise sound research.

Data in the life sciences generally falls into three broad categories: continuous, categorical, and time-series. Continuous data includes measurements such as weight, temperature, concentration, or gene expression levels. Categorical data includes groupings such as treatment conditions, genotypes, sex, or disease status. Time-series data tracks measurements across ordered time points, such as daily weight gain, weekly pathogen levels, or monthly milk production.

Each data type carries distinct structural properties that influence which visualization approach will work best. Continuous data has a natural numerical order and spacing, making it suitable for plots that preserve quantitative relationships. Categorical data has discrete groups without inherent numerical spacing, so visualizations must emphasize comparisons between groups instead of numerical continuity. Time-series data has an inherent temporal order, so the visualization must preserve that sequence to reveal trends, cycles, and changes over time.

The choice of visualization also depends on the specific question you are asking. A researcher examining the distribution of a single continuous variable needs a different chart than one comparing means across treatment groups. A clinician tracking patient outcomes over several visits needs a visualization that emphasizes change, beyond final values. Before selecting a chart type, define the primary question your figure must answer.

The National Library of Medicine Research Methods Resources provides access to authoritative texts on research methodology that can help clarify how data structure relates to appropriate analysis and presentation. Consulting such references early in the figure design process can prevent common visualization errors.

At a Glance: Matching Chart Types to Data Structures

This table provides a starting point for chart selection. The sections below explain the reasoning behind each recommendation and describe common pitfalls associated with each approach.

Visualizing Continuous Data

Continuous data represents measurements on a numerical scale where the intervals between values carry meaning. Body weight in kilograms, rectal temperature in degrees Celsius, and serum antibody titers are all examples of continuous measurements common in animal research. The primary visualization goals for continuous data are showing the distribution, revealing relationships between variables, and identifying outliers or unusual values.

Scatter Plots for Relationships Between Continuous Variables

When the research question involves the relationship between two continuous variables, a scatter plot is often the most informative choice. Each point represents one observation with its position determined by the values of the two variables. This chart type reveals whether the variables move together, whether the relationship is linear or curved, and whether any points fall far from the general pattern.

For example, a researcher investigating the relationship between feed intake and weight gain in growing pigs could plot daily feed intake on the horizontal axis and weight gain on the vertical axis. The resulting scatter plot would show whether higher feed intake consistently corresponds to higher weight gain, and whether any animals deviate substantially from the overall pattern.

Scatter plots also support the addition of a fitted line or curve to summarize the central tendency of the relationship. However, the fitted line should never obscure the individual data points. Overlapping points can hide the true density of observations, so consider using transparency or jittering when many points occupy the same region of the plot.

Histograms for Distribution of a Single Continuous Variable

When the research question concerns the distribution of a single continuous variable, a histogram provides a direct view of how values are spread across the measurement range. The horizontal axis represents the variable of interest, divided into bins, and the vertical axis shows how many observations fall within each bin.

Histograms reveal whether the data are symmetric, skewed, bimodal, or concentrated around a central value. This information matters for choosing appropriate statistical methods and for identifying data entry errors or unusual observations. A histogram of body weights across a research herd might reveal two distinct peaks, suggesting that the population actually contains two subgroups that should be analyzed separately.

The choice of bin width substantially affects the appearance of a histogram. Very narrow bins produce a jagged plot with many empty intervals, while very wide bins can hide important features of the distribution. Experiment with several bin widths and select the one that reveals the structure of the data without introducing artifacts.

Box Plots for Comparing Continuous Data Across Groups

When continuous data are collected from multiple groups, a box plot provides a compact summary of the distribution within each group. The box shows the interquartile range, the line inside the box shows the median, and the whiskers extend to the most extreme values that are not considered outliers.

Box plots are particularly useful when the number of groups is large or when the sample sizes within groups are small. They allow quick visual comparison of central tendency and spread across many groups in a single figure. However, box plots do not show the actual data points, so they can hide multimodality or unusual patterns within a group. When sample sizes are small, consider overlaying individual data points on the box plot to preserve the full information.

Visualizing Categorical Data

Categorical data consists of observations that fall into distinct groups or categories. Treatment conditions, breed classifications, sex, and disease status are common categorical variables in animal research. The visualization goals for categorical data are comparing group outcomes, showing proportions, and communicating the uncertainty associated with group estimates.

Bar Charts for Group Comparisons

Bar charts are the standard choice for comparing a continuous outcome across categorical groups. The height of each bar represents the group mean or median, and the bars are arranged along the horizontal axis according to the categories. This arrangement makes it easy to compare group values at a glance.

A common error in bar chart design is showing only the bar height without any indication of the variability within each group. A bar chart that shows only means can make two groups appear identical when their underlying distributions overlap substantially. Add error bars showing the standard error or confidence interval, and overlay individual data points when sample sizes are manageable.

For example, a study comparing average daily gain across three dietary treatments should show the mean for each treatment group, the variability around each mean, and the individual animal values when feasible. This approach allows readers to assess both the central tendency and the spread within each group.

Dot Plots as an Alternative to Bars

Dot plots offer a useful alternative to bar charts for categorical comparisons, particularly when sample sizes are small. Each dot represents an individual observation, and the dots are arranged vertically above each category label. This format shows the full distribution of values within each group, including gaps, clusters, and outliers.

Dot plots avoid the visual weight that bars can impose and make it easier to see the actual data structure. They are especially valuable when the number of observations per group is small, because the reader can see every data point instead of relying on summary statistics alone.

Stacked or Grouped Bars for Proportions

When the research question involves the proportion of observations falling into subcategories within each group, stacked or grouped bar charts can communicate the breakdown effectively. A stacked bar chart shows the total for each group divided into segments representing the subcategories. A grouped bar chart places bars for each subcategory side by side within each group.

Stacked bars work well when the primary question concerns the total and the composition of that total. Grouped bars work better when the primary question concerns comparing the subcategory values directly across groups. Choose the format that matches the specific comparison your audience needs to make.

Visualizing Time-Series Data

Time-series data tracks measurements across ordered time points, such as days, weeks, or months. This data type appears frequently in animal research, including studies of growth over time, disease progression, treatment response, and seasonal patterns. The defining feature of time-series data is the temporal order, and the visualization must preserve that order to reveal trends and changes.

Line Charts for Trends Over Time

Line charts are the standard choice for time-series data because they connect successive time points with lines, emphasizing the sequence and the direction of change. The horizontal axis represents time, and the vertical axis represents the measured variable. This format makes it easy to see whether values are increasing, decreasing, fluctuating, or remaining stable.

When multiple groups are compared over time, plot each group as a separate line on the same chart. Use distinct colors or line styles to differentiate the groups, and include a clear legend. For example, a study comparing weight gain in animals receiving two different feed formulations could plot the average weight for each group at each time point, with one line for each treatment.

Handling Repeated Measurements

Time-series data in animal research often involves repeated measurements from the same animals across multiple time points. This structure introduces correlation between observations from the same animal, which affects both statistical analysis and visualization. When plotting individual animal trajectories, use faint lines for each animal and a bold line for the group average. This approach shows both the individual variation and the central trend.

Common Pitfalls in Time-Series Visualization

One common error is using a bar chart for time-series data. Bars imply that the time points are independent categories, which obscures the continuous nature of change over time. Another error is connecting time points that are not equally spaced, which can distort the apparent rate of change. Ensure that the horizontal axis spacing reflects the actual time intervals between measurements.

A third error is plotting too many lines on a single chart, creating a confusing tangle that obscures the patterns. When the number of groups or individuals is large, consider faceting the plot into multiple panels, with each panel showing a subset of the data.

Practical Workflow for Figure Design

Designing effective figures requires a systematic approach that begins before any plotting software is opened. The following workflow provides a structured path from raw data to a publication-ready figure.

Step 1: Define the Research Question

Write down the specific question the figure must answer. This question determines the data type emphasis and the appropriate chart type. A figure that tries to answer multiple questions simultaneously often fails to answer any of them clearly.

Step 2: Examine the Data Structure

Review the dataset to confirm the data types of the variables involved. Check for missing values, outliers, and coding errors before creating any visualization. A figure built on uncleaned data will mislead readers regardless of how well the chart type matches the data structure.

Step 3: Select the Chart Type

Use the decision table in the At a Glance section to select a starting chart type based on the data structure and research question. Consider whether the chart preserves the essential features of the data, including distribution, spread, group differences, or temporal trends.

Step 4: Create a Draft Figure

Generate a draft figure using your preferred plotting software. Examine the draft critically for whether it reveals the patterns you intend to communicate. If the chart obscures important features, try an alternative chart type or adjust the parameters such as bin width, axis scaling, or point transparency.

Step 5: Refine for Clarity

Adjust labels, axis ranges, colors, and annotations to make the figure self-explanatory. The reader should understand the figure without referring to the text for basic interpretation. Ensure that axis labels include units, legends identify all groups, and any annotations are clearly explained.

Step 6: Test the Figure on a Colleague

Show the draft figure to a colleague who is not familiar with the project and ask them to describe what they see. If their interpretation differs from your intended message, revise the figure accordingly. This step often reveals assumptions that are obvious to the researcher but invisible to the reader.

Records and Measurements for Figure Quality Control

Maintaining records of figure design decisions supports reproducibility and helps identify recurring problems. Keep a log that documents the chart type selected, the rationale for that selection, the software and parameters used, and any revisions made during the design process. This record becomes valuable when preparing figures for publication or responding to reviewer comments.

Track the following measurements for each figure you produce:

These records support the transparency expectations described in the Committee on Publication Ethics Core Practices, which emphasize the importance of accurate and complete reporting of research methods and results.

Common Failure Patterns in Figure Design

Recognizing common failure patterns helps researchers avoid errors that undermine the effectiveness of their figures. The following patterns appear frequently in scientific publications and presentations.

Using One Chart Type for All Data

The most common failure is applying a single chart type to every dataset regardless of its structure. A researcher who uses bar charts for everything may obscure the distribution of continuous data, hide temporal trends, or fail to show relationships between variables. Match the chart type to the data structure instead of defaulting to a familiar format.

Overloading the Figure with Information

Figures that attempt to show too many variables or groups become cluttered and difficult to interpret. When a figure requires extensive explanation to understand, consider splitting it into multiple panels or figures. Each figure should communicate one clear message.

Ignoring the Data Points

Summary-only charts that show means or medians without any indication of the underlying data can mislead readers. Variability within groups is often as important as the central tendency, and hiding that variability can lead to incorrect conclusions. Show individual data points whenever sample sizes permit.

Misleading Axis Scaling

Axis scaling that starts at a nonzero value can exaggerate small differences between groups. Conversely, axis scaling that is too wide can compress meaningful differences into an unreadable range. Choose axis limits that represent the data honestly without distorting the visual impression.

Poor Labeling and Legends

Figures without clear axis labels, units, and legends require readers to search the text for basic information. This burden reduces the accessibility of the figure and can lead to misinterpretation. Label every axis with the variable name and units, and ensure that all groups are identified in a legend or directly on the plot.

Welfare and Safety Context in Figure Design

Figures in animal research often present data that carry welfare and safety implications. Growth rates, disease incidence, mortality, and behavioral observations all relate directly to animal welfare. The way these data are visualized can influence how readers interpret the welfare status of research animals.

When presenting welfare-related data, choose visualizations that reveal the full range of outcomes instead of only summary statistics. A figure showing individual animal weights can reveal animals that fall below expected growth trajectories, prompting welfare review. A figure showing only group means may hide these individuals.

The National Institutes of Health Grants and Funding pages describe the expectations for rigorous experimental design and transparent reporting in NIH-supported research. These expectations extend to the presentation of data in figures, which should accurately represent the observations without distortion.

Researchers should also consider the NIH Data Management and Sharing Policy when preparing figures, because the underlying data may need to be shared in a form that allows others to verify the visualizations. Figures that cannot be reproduced from the shared data undermine the transparency that the policy seeks to promote.

Limitations of Visualization Choices

Every visualization approach has limitations, and acknowledging these limitations strengthens the credibility of the research. A scatter plot may hide the density of overlapping points. A histogram depends on the chosen bin width. A box plot obscures the shape of the distribution within groups. A line chart may imply continuity between time points that were not actually measured.

Researchers should consider whether the chosen visualization accurately represents the data structure or whether it introduces artifacts. When the limitations of a chart type could affect interpretation, mention those limitations in the figure caption or the accompanying text.

The EQUATOR Network provides access to reporting guidelines that help researchers present their methods and results transparently. Consulting the relevant reporting guideline for your study type can identify aspects of data presentation that reviewers and readers expect to see.

Professional Escalation Criteria

Some figure design challenges require consultation with colleagues, statisticians, or other professionals. Consider seeking additional input in the following situations:

The ORCID for Researchers pages describe how maintaining a persistent researcher identifier supports the accurate attribution of your published figures and other research outputs. Ensuring that your figures are associated with your correct identity supports the integrity of the scientific record.

Frequently Asked Questions

How do I choose between a bar chart and a box plot for grouped continuous data?

Use a bar chart when the primary message is the group means and you want a simple visual comparison. Use a box plot when the distribution within each group matters, including the spread, median, and potential outliers. For small sample sizes, overlay individual data points on either chart type.

What chart type should I use for data collected over multiple time points?

A line chart is the standard choice for time-series data because it preserves the temporal order and reveals trends and changes over time. Ensure the horizontal axis spacing reflects the actual time intervals between measurements.

How can I show both individual data points and group summaries in one figure?

Overlay individual data points on a bar chart or box plot. Use transparency or jittering to reduce overlap when many points fall in the same region. This approach shows both the central tendency and the full distribution of the data.

What is the best way to visualize the relationship between two continuous variables?

A scatter plot is the most informative choice for showing the relationship between two continuous variables. Add a fitted line or curve to summarize the central tendency, but do not let the fitted line obscure the individual data points.

How do I handle overlapping points in a scatter plot?

Use transparency to make overlapping points visible, or apply jittering to spread points slightly. When the number of points is very large, consider a density plot or hexagonal binning to show the concentration of observations.

Should I always include error bars on bar charts?

Error bars showing the standard error or confidence interval provide important information about the precision of group estimates. However, error bars alone do not show the distribution of the data. When sample sizes are small, overlay individual data points in addition to error bars.

What should I do if my figure looks cluttered with too many groups?

Consider faceting the figure into multiple panels, with each panel showing a subset of the groups. Alternatively, select a smaller number of groups to highlight in the main figure and move the remaining groups to supplementary material.

How do I make sure my figure is accessible to readers with color vision deficiency?

Use color schemes that remain distinguishable for readers with common forms of color vision deficiency. Add shape or line style differences in addition to color, and test the figure using a color vision deficiency simulator before finalizing it.

Related Articles

• Designing Effective Qualitative Surveys: Question Types and Examples
• Data Visualization for Biological Research: Choosing Figures That Preserve Meaning
• How to Choose the Right Control Type for Your Experiment
• Data Management Plan Template: A Practical Guide for Researchers
• Data Management Platform: What It Is and How to Choose One

References and Further Reading

• Research Methods Resources. National Library of Medicine.
• EQUATOR Network. EQUATOR Network.
• Core Practices. Committee on Publication Ethics.
• NIH Grants and Funding. National Institutes of Health.
• ORCID for Researchers. ORCID.
• Data Management and Sharing Policy. National Institutes of Health.
• NCBI Literature Resources. National Center for Biotechnology Information.
• PubMed. National Library of Medicine.

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