Titles #
last modified February 24, 2026
~6 minute read
On the page considering the point of data visualizations, I proposed that when constructing a graphic for an audience, your core objective is to illuminate the underlying meaning within the numbers. You have a specific narrative to communicate or a definitive claim to make regarding the data’s implications. Consequently, the guiding principle for titling data visualizations is remarkably straightforward: explicitly state your claim.
Your title should make a claim #
It is instructive to explore what this means in practice, primarily because there are pervasive examples of this rule being ignored, which leads many designers to feel uncomfortable making direct assertions.
Do not simply state what the data is #
A very common thing to see is a title that simply states what is being plotted. In science you might see a title like “UV-vis spectrum of <insert molecule>.” In journalism you will find titles like “change in housing prices over time” on a plot like that below.
I believe titles of this nature are frequently advocated under the misconception that the fundamental role of a data visualization is to present data entirely without bias. My comprehensive response to this fallacy is detailed in the module on the point of data visualizations. In short, your objective should be to actively guide the audience to see what you see in the data. Because this is the true goal, you must deploy every available design tool to fulfill it. Due to standard reading conventions, the audience will almost always consume your title first. If you are not leveraging the title to establish your central thesis immediately, you are squandering a critical communicative opportunity.
Of course, this still requires understanding how to best leverage the title.
Do not ask a question #
Another idea you will see is to pose a question in the title. In science, you might see something like “Does <insert molecule> absorb in the NIR?” There are a number of problems with asking a question. For one, a person may not reach the same conclusion you want them to. So, in order to ensure that they answer the question they way you are hoping, you need to make the question so painfully obvious that people might be insulted. For instance, we might turn the title for the above plot into a question: “Are housing prices in the USA increasing?”
If you try something more subtle, like “Is the housing price in the USA increasing exponentially?” You might have people reach different conclusions.
Additionally, there is always the temptation to ask a question that the data is not really able to answer, which is also a bit frustrating. For instance, in trying to connect with the reader, you might ask a question like “Is now the right time to buy a house?” But this plot simply cannot answer this question. Sure, there is currently a dip in the housing price (at least at the end of this chart), but there are many other factors that go into buying a house, rather than just price.
Despite these problems, asking questions in titles is still pretty common. I think the idea is that it invites the reader to explore the data, and therefore engage with the data more. However, I think in practice, it is less effective than making a claim. Making a claim implicitly poses a much more engaging question: “Do I agree with the creator of this data visualization?”
Making a claim #
When articulating a claim, it should be as concise as possible. I strongly recommend utilizing a robust, declarative sentence structure: noun-verb-object. In a scientific context, this translates to “<*insert molecule> absorbs in the NIR.” In journalism, it becomes “Housing prices in the USA have increased persistently over time.”
The advantage of this format is that the main point of the visualization is clear. Additionally, it guides the reader on what aspects of the data to focus on. Lastly, it assigns the reader a task: decide if they agree with the claim. This invites much more critical analysis of the visualization.
There are a few guidelines for generating a well-formed claim. They are:
- Your claim should be be falsifiable. That is, it should be stated in such a way that it is logically possible for the viewer to disagree. So, tautologies are not good titles.
- Your claim should not require support from outside the data visualization. That is, the viewer should not need to look for information outside of the data that is labeled by the title.
- The data visualization should provide sufficient information for the viewer to make a judgement about your data. Combined with the guideline above, this means that the data visualization is both complete and sufficient. This last point is important, and so, let us consider it in more detail next.
Your data visualization should provide evidence supporting this claim #
One way to interpret this instruction is that you should only make claims that the data supports. While this is a good rule to follow, it overlooks another powerful aspect of making a claim about the data. If you make a claim, it will provide you guidance on how to ensure that you are designing the data visualization to support this claim.
Consider that we wish to apply a slightly different title to our ongoing housing dataset example. Perhaps the specific claim we want to advance is: “Housing prices in the USA increase exponentially over time.”
Looking at our previous baseline figure, we do not inherently possess sufficient visual evidence to validate that specific mathematical judgment; therefore, making the claim dictates that we must upgrade our visualization. We can introduce additional analytical evidence, such as fitting an exponential growth curve directly to the data, alongside a statistical measure of the model’s fidelity. While multiple statistical options exist, $R^2$ is widely recognized by diverse audiences and serves as a highly effective visual anchor.
Other considerations for titles #
There are just a few other things that one might consider when making a title.
Alignment #
A standard practice is to place the title in the top left, which is all well and good. However, there is one problem with this, from the perspective of alignment. Basically, we normally expect to see text left-aligned (which it is), but there is also a very strong indicator of where to align this, in terms of the y-axis line. Thus, I think it makes more sense to move the title to be aligned on the axis.
This sort of alignment creates a more firm connection between the title and the data visualization is labels.
Color #
Another place that you can draw a better connection between the title and the data is to color-code the words in the title to the data it refers to. For instance, “housing prices” refers to the data represented by the blue trace, while “exponentially” refers to the orange fit line. We can recolor the words in the title accordingly.
Key takeaways #
Further reading (a.k.a. evidence for my claims) #
The basic format suggested here is inspired by a paradigm in scientific presentations called “Assertion-Evidence.” And there is an entire website devoted to it. It was developed, in part, by a engineering professor at Penn State, Richard Alley, who has also written multiple papers and a book on the topic. His work focuses on the assertion-evidence model in the context of presentations (which I highly recommend using), but I find that it also provides a great framework for thinking about titles for data visualizations.
page last modified February 24, 2026