When we ask our students to collect, evaluate, and analyze data, our instructional focus often first falls on ensuring they have the tools they need to interpret data. We teach students how to identify patterns, explore relationships, and assess comparisons.

Yet moving from the ability to understand data representations to the ability to effectively incorporate data into an argument can mark a threshold concept in data literacy. This means that once a student learns how to incorporate data in an argument, it can permanently and dramatically change their perception of what data is and does—often leading to more precise understanding and deeper critical thinking.

Ensuring students can deploy data to suit their ends in multiple contexts and genres—from research reports and presentations to editorials and policy briefs—is thus a key task for instructors across disciplines. But communicating effectively with data is no easy task, even for the most experienced writers. Here are three common problems found in novice writing with (and about) data, and some tips for how to help students overcome them.

Problem: Saying too little. Students think that data “speaks for themselves.”

Solution: Help students identify their purpose—and make it explicit.

The prospect of writing about data can sometimes overwhelm students. The technicalities involved with statistical evidence, for example, or the variety and richness of data visualizations available, may make inexperienced writers feel lost in the weeds when they attempt to discuss their data. Rather than putting in the effort to get back on the right course, they might reasonably decide that the path of least resistance is to ‘let the data speak for themselves.’ This is a common problem in undergraduate writing, in which students include data in the text but ultimately say little, if anything, about what they think it means. 

Faced with this situation, instructors may find it useful to demonstrate to students that everything’s an argument—even discussions of data and findings. Much writing in the social and natural sciences, of course, aims for a tone of impartiality with its interpretation of data. But this doesn’t mean that there is no argument—writers are always taking positions as they decide what they want readers to take away from their text.

Experienced writers know that, far from speaking for themselves, nearly all data can be interpreted multiple ways. Impressing on students the importance of articulating their interpretation as an implicit argument—i.e., that their interpretation is correct—can go a long way in rectifying this problem.

A good first step may be to help students identify what their purpose is. Getting a student to recognize that data is (or should be) included for a reason—to present a pattern, for example—can help them understand that an effective discussion must explicitly tell the reader what that pattern is, and why it matters.

Problem: Saying too much. Students try to explain every datum.

Solution: Help them subordinate their evidence to their purpose.

Some students will err in the other direction, choosing to say too much about their evidence—including excessive information or explaining every datum. This may lead to a discussion that is irrelevant (at best) or incoherent (at worst).

To find the happy medium between “saying too little” and “saying too much,” instructors should help students lean into their purpose. When selecting data or figures to include and discuss, students should do so in a way that serves the broader goals of the assignment. Including a piece of evidence for its own sake will distract readers and muddy the analysis.

Giving students examples of various uses of data and figures that are purpose-driven is a useful strategy. These purposes include, for example:

• Presenting patterns (such as distribution, composition, or change over time)
• Demonstrating relationships (such as correlation or other associations)
• Showing comparisons (such as those between or among two or more categories or variables)

Notice the verbs used: present, demonstrate, and show. Instructors should help students understand that this is about communication, not exploration. Often, students have difficulty thinking about the written product as a separate step from the research itself. In the research stage, students will use data to identify patterns, explore relationships, and assess comparisons. But at the presentation stage, the goals are not to ‘show their work’ but rather to ‘show what they found.’

Instructors can design assignments and exercises to reinforce the message that all writing about data should be subordinated to a purpose. For example, Deborah Nolan, author of Communicating With Data, designs technical assignments that ask students to take on a problem from varying perspectives. As students work to craft technical discussions of data for different purposes—such as a consumer guide or a memo to a supervisor—they learn to identify how these divergent purposes shape various approaches to writing about data. An infographic assignment can also be a good way to practice these skills, as the minimalist form forces authors to be selective about what data they display. 

Problem: Overstatement and oversimplification.

Solution: Lean on complexity to enrich students’ writing.

As they try to strike a balance between saying too much and saying too little, students may ignore qualifying or complicating evidence for the sake of clarity. Inexperienced academic writers might reasonably think that evidence which complicates their argument also complicates their writing. They may unintentionally succumb to cherry picking evidence—not because they are trying to be deceitful, but simply because they are trying to articulate their point clearly.

Experienced writers know that it’s possible to address complicating data while still subordinating it to the writer’s purposes. Our job as instructors is to help students see that doing so can make arguments even stronger.

One helpful exercise may be to have students explain not why their explanation is convincing, but rather why their explanation is more convincing than the alternatives. This slight change in phrasing forces students to grapple with complicating evidence in their assignments, rather than letting it fall by the wayside. In doing so, they may find not only that their arguments are stronger, but also that their ability to write clearly and persuasively about data has improved.

In Chris Carroll’s course “Tools for Writing a Research Paper in Economics,” featured in the Model Library, students compose a semester-long research paper that integrates data visualizations to support their argument.