A Guide To Accurate And Good-Looking Figures

In this article, imagine you are about to publish a paper – perhaps even in one of the IUBMB journals?

_{I bet you would stop and look at these figures when you encounter them. They come from a Nature and a Science publication – click to enlarge to take a closer look at the latter.}

While many researchers simply load their data into a statistics program, choose a few colors they like, and export the figure, this approach often produces results that do not look professional and may even mislead the reader.

In other words, the figures may lack scientific precision.

This problem often arises because, at university, we do not learn statistics in depth, nor do we receive training in visual design.

However, both are quite simple to master

We just need to pay attention to a few key concepts. Let’s see how you can create excellent figures in today’s research environment.

TL;DR

This article takes about 5–10 minutes to read. I kept it as concise as possible and cover how to design effective figures by:

Deciding which data and statistics to show
Choosing the appropriate graph type
Adjusting display settings (points, lines, ticks, colors)
Designing statistical elements accurately

I outline best practices, practical tips, and include a range of exemplary graphs.

Whether you read it carefully or skim through it, both approaches should provide a lot of value.

Why Graphical Design Matters

How data are visualized directly affects how they are interpreted.

= Poor design can hide variability, exaggerate effects, or even lead to incorrect conclusions, making graphical design essential for scientific accuracy.

_{Imagine that six clinical trials, each with 30 participants, were conducted. If you only had three minutes to read the entire paper, it is unlikely you would draw the same conclusion when shown the graph on the left compared to the one on the right. Irrespective of the mean, the two populations only become evident in the right figure.}

Design also shapes trust. Readers subconsciously judge the credibility of a study by the clarity and professionalism of its figures. Confusing or careless visuals can undermine confidence in otherwise solid data.

_{Could you reliably tell whether the dark blue bar is higher than the dark green one in the left figure? Nevertheless, the disharmonious choice of colors and the missing borders around the bars suggest that the graph on the left is less “professional.”}

Finally, whether in posters or proposals, clear, well-designed graphics are far more likely to capture attention, be understood quickly, and be remembered.

Choosing The Right Graph Type

Most often, we default to those graph types we are used to.

A more scientific approach might be to start by defining the main message of each figure and the order in which information should be presented.

This clarifies what they should convey (and helps to determine their sequence in your paper).

How To Choose

To find an appropriate figure type, you want to ask two questions:

A) Will it display all relevant information? Think

Distribution
Summary statistics such as means (i.e., effects & quantities)
Inferential statistics (SD, CI, or significance)
Inter-sample patterns or comparisons

B) What is visually the easiest to interpret?

What makes differences or similarities most clear?
What guides the eye of your reader?
What can be quickly screened?

Considering Digestability

A good design guides the eye and minimizes how much information the reader has to hold in working memory. Legends are a classic example where smart design can make a big difference.

_{This example is taken from an excellent overview titled “Design Strategies for Scientific Figures” by the College of Natural Science at the University of Texas at Austin. As a rule of thumb, the faster you can understand a figure, the less mental effort is required.}

Sometimes, it’s about applying principles of visual composition that help readers grasp information quickly:

_{As outlined by Aiora Zabala in “Designing More Effective Scientific Figures” (VTP Graphic Design, Cancer Research UK), grouping, ordering, and containing can help readers analyze data at a more hierarchical level and therefore process it faster.}

Knowing Which Graphs Exist

After decades of research, new graph types are still getting “invented”.

However, even the fundamentals are not always properly taught in university. Therefore, here is some helpful resources:

_{Ardigen provides a nice walkthrough of the graph types available and also offers a clear overview that you can keep or turn into a poster.}

Luzmo provides a solid overview of the basic chart types you should know.
Atlassian compiled an almost exhaustive overview, with more in-depth explanations of each chart type.
And Datawrapper offers a clear overview along with practical design tips.
^{_{PS: Make sure to check the small “Example” and “How To” links at the end of each paragraph. For instance, you can dive deeper into when to use area charts or learn how to turn donut charts into bar charts.}}

The Data Visualization Catalogue also has a helpful search page where you can look up suitable graph types based on what you want to display.

Core Design Principles

Axis Dimensions

Once you compiled your data and chosen a figure type to display it, double-check whether your statistics program automatically chooses the range of your y-axis.

If your y-axis range is too large, differences between groups become visually harder to see, potentially misleading your audience.

Next, think about the dimensions of your graph in terms of the x-axis. Make sure your readers can visually assess all data points quickly.

_{When it comes to curves, consider that the spacing of your x-axis has an important impact on the perceived slope of your graph. Again, there is no way around this. Also consider that the perception of your error bars will vary with the size of your graph. In both cases, mean values ± SD are displayed.}

If you squeeze them too close together, it will become difficult. If you stretch the axis too long, it becomes harder to compare data points and differences.

Axis & Data Labels

Your axis labels should allow readers to immediately understand what is being displayed – even without reading the description or main text.

This is especially important because many readers simply scroll through figures to judge relevance.

Pay attention to where you place your legend when you have large panels. Don’t underestimate how long it feels when your eyes have to travel across several charts just to double-check the legend.

In complex setups, it can be helpful to add an additional panel showing the experimental setup.

Axis Ticks

When defining axis ticks, aim for clarity rather than minimalism or maximalism.

Ticks should help readers understand where bars or data points lie.

Line Thickness & Patterns

An often overlooked topic of utmost importance is line thickness. Several scientists might not even know that they can edit it in most software.

When using bar graphs you can choose patterns. But be careful, avoid overwhelming your reader.

When patterns don’t fit, consider differentiating through color – since we live in 2025, you don’t need to worry that your figures will be printed in black and white.

Color Harmony

One major issue is that we often believe we can choose colors arbitrarily.

But colors strongly influence how data is perceived. And without other supporting graphical elements, we cannot simply “overcome” biasing color.

Rainbow color schemes, for example, draw attention unevenly, and distract the eye. Light tones such as bright yellows are more difficult to see.

Therefore, choose a harmonious color palette. We discussed several tools that help with this.

Accounting For Statistics Properly

How statistics are displayed is critical to scientific accuracy – often as important as the analysis itself.

Effective visualization rests on two principles: show as much relevant information as possible (for example, individual data points), and avoid biasing the viewer through design choices.

Poor or selective presentation can mislead, especially when the presenter has a preferred interpretation. The goal is to let readers draw their own conclusions.

_{Ever wondered why you normally don’t see pie charts in scientific publications? Because they are suboptimal to read. A bar chart is much better for that purpose. This website has an amazing “game” that tests your ability to analyze different graphs.}

Statistics should be clear and informative but visually secondary, supporting the data rather than dominating it.

Adding information such as a barcode graph can help leverage the specific advantages of a given visualization technique and thereby allow the reader to gain a better understanding of the data.

_{The graph above taken from this blog shows the dependency of cell lines on the gene FOXA1 – those to the left of the minus-1 reference line require the gene to survive. Note that for lower frequencies of cell lines, the barcode graph is advantageous, whereas for high densities, the bell curve excels.}

Subtle design choices, such as lighter colors and thin lines, help communicate information without distraction.

_{This is what I mean – the confidence bands make the graph more informative while also improving its appearance. More about such tweaks in this amazing blog.}

And still, at some point, design and statistical expertise mix.

For instance, imagine we have to decide which variability measure to show. In bar graphs, you typically choose one of the following: standard deviation, standard error of the mean, or confidence intervals.

Remember, many scientists do not clearly understand the differences, and even fewer take the time to check which one is shown.

A Final Takeaway

OK, so how do you design figures properly?

What is the key message(s) of this figure?
Which data or samples do you need to show, and which statistics should you include?
What is the right graph type (e.g., does it properly show the data distribution)?
Which settings fit your data—should you show individual data points? What line thickness and tick frequency are appropriate?
Choose colors wisely and keep them consistent throughout your paper and across samples.

If you follow these five steps, your figures will look better than the majority of those printed in papers.

Bonus: When designing figures for presentations or posters, it is acceptable to leave out some samples and use larger labels or thicker lines. Again, put yourself in the shoes of your viewer!

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By Patrick Penndorf (LinkedIn)