Data Interpretation: Start by Understanding the Graph
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In each Quant section on the GRE, you’ll see three questions that ask about a graph or pair of graphs; these are the Data Interpretation (DI) questions. All three questions usually refer to the same graph(s) and show up about two-thirds of the way through the section.
I find that students tend to love or hate these Data Interpretation questions. For some, they feel comfortable and routine, a nice respite from mathematically trickier questions elsewhere in the section. Others, however, find them overwhelming; there’s too much information, and it takes too long to sort through it.
Both types of students are right. On the one hand, the actual calculations involved in Data Interpretation questions are often quicker and more straightforward than those in regular Quant questions. On the other hand, there is a lot of extraneous information in these problems, and they can be big time wasters.
My strategy for working with these graphs is similar to the one I use with long passages in Reading Comp: I understand the main idea and outline of the passage upfront, then I get into the details when and if a question asks about them. So, each time I get to the first Data Interpretation question, I take some time to unpack the graph before diving into the questions.
First, I identify what type of Data Interpretation graph I’m working with.
This will already tell me a lot about what kind of information I’m going to get. For example, if I see a bar graph, then I know that I’ll be able to quickly assess the relative sizes of different categories, as in this example:
Conversely, if I see a pie chart, I know I’ll get information about how a whole is divided into parts. I may or may not get information about the actual numbers involved. For example, in this pie chart, I can tell that there are more pitbulls than pugs, but I can’t tell exactly how many I have of either. In general, I want to pay attention to whether I’m getting actual numbers or just percents. If a chart gives me percents, like this one here, then I can’t say anything about actual numbers without also being given a total number.
Some other common types of graphs that you should be familiar with are line graphs, scatter plots, and stacked bar graphs.
Second, I read the title.
This is an important step that many students skip over—don’t! The title can give you very useful information about how to read the graph. In the graphs above, for example, the titles alone tell me that I’m going to get information about how all of the dogs in the park divide into different breeds.
Third, I check my axes and labels.
I see what units I’m working with and what categories are being compared. In my bar graph, for example, I have the number of dogs in my Y axis, compared to breeds in the X axis. I also check to see if I’m dealing with actual numbers, with percents, or with both.
Fourth, I’ll pick one point on the graph and explain what that point means.
For example, the red slice on my pie chart tells me that 15% of all dogs at the dog park are pugs. On a straightforward graph like this one, this step may feel unnecessary. Graphs on the GRE, however, are seldom as simple as the examples I’ve given here. Explaining one point allows me to feel confident that I’ve understood how the graph works without my having to look at the overwhelming mass of information that’s being presented in the graph.
These steps take a bit of time upfront. However, once I’ve understood my graphs, I’ll then be able to locate any information I need to answer the questions quickly and correctly. Since the actual calculations involved won’t be as time-consuming as in other Quant problems, taking time to understand the graphs will make my work with Data Interpretation questions more efficient overall.
Data Interpretation questions can reliably boost your score if you approach them in a consistent and methodical way. Take your time upfront and don’t let the mass of information intimidate you. ?
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Cat Powell is a Manhattan Prep instructor based in New York, NY. She spent her undergraduate years at Harvard studying music and English and is now pursuing an MFA in fiction writing at Columbia University. Her affinity for standardized tests led her to a 169Q/170V score on the GRE. Check out Cat’s upcoming GRE courses here.