# Data Interpretation on the GRE Quant

## Data Interpretation on the GRE Quant

On test day, when you sit down to take your GRE, you will have, among other things, two Quant sections. Each Quant section will have a Data Interpretation problem set towards the end of the section. This GRE Data Interpretation set will present data, information, in some graphical form, and it typically has three questions about the same data. So, you will see about 3 DI questions on each GRE Quant section, so about 6 DI questions on your test—or more if the experimental section is also a Quant section. As you can see, Data Interpretation practice is an important part of your GRE Quant prep.

## Why Data Interpretation?

Why is there Data Interpretation on the GRE? Why is this important? Well, do you know the old adage “A picture is worth a thousand words“? Well, a graph is worth even more. Geeky math & techie folks, such as I, absolutely love graphs and charts, because they present an efficient means to convey a truckload of information in way that is directly visually accessible. In our post-modern electronic world, the sheer amount of information available is simply mind-boggling. Graphs and charts are essential for keeping track of all this information.
Here’s one big suggestion for GRE Data Interpretation: if you are not a geeky math or techie person, start looking at graphs & charts. Look in the financial news, in scientific articles, and in international news in general. Most newspapers and news magazines are stuffed to the guppers with graphs & charts. Spend time studying them: each graph, each chart, has a “story” to tell. Spend enough time with each to understand its “story.”
Despite appearances, the GRE Data Interpretation questions are usually relatively easy compared to the rest of the questions on the Quant section. The whole point of graphs is to make information easy to see!
What You Need to Know for GRE Data Interpretation
The GRE Data Interpretation will present information in any one of a number of visual formats. These include:
(a) pie charts
(b) bar charts
(c) line graphs
(d) scatterplots & best-fit lines
(e) boxplots
(f) histograms (which are different from bar charts!!)
(g) charts of numerical data
Of these, pie charts and line charts and charts of numerical data are probably self-explanatory, and there are examples below (pie chart & bar chart in Set #1, line chart in Set #2). Scatterplots and boxplots are somewhat less well-known, and each has its own blog giving more detail; there’s also an example of a scatterplot below, in Set #4.
I will say a few words here about bar charts vs. histograms. First of all, on a bar chart, there’s no particularly important difference whether the bars go horizontal or vertical (a.k.a. a “column” chart); this choice is more a matter of artistic preference in display, but it’s not meaningful for the data. In a bar chart, each bar represents a different item or group. The scale of some numerical variable is parallel to the bars, and the purpose of the graph is to compare the numerical values of the elements represented by different bars.
**Answer = E**.