GRE Quantitative Reasoning - Data Interpretation Sets
In the Quantitative Reasoning sections of the GRE, some questions are grouped together and refer to a common table, graph, or other data presentation, with questions involving interpretation or analysis of the provided data. These questions may be Numeric Entry, Multiple Choice (one answer), or Multiple Choice (one or more answers). All of the questions in a Data Interpretation Set are based on the same data. In addition to Data Interpretation Sets, the Quantitative Reasoning sections of the GRE will feature some discrete individual questions.
Data Interpretation sets mimic a format that will likely be common in graduate or business school, requiring students to analyze and utilize presented information to answer discrete questions. One example of problems like this would be a business reviewing its performance data to evaluate the percentage change from one time period to another, using the information to revise its strategy and reallocate its resources. Students preparing to solve Data Interpretation Sets will be reviewing and learning skills that will benefit them throughout their careers.
When preparing to solve a Data Interpretation Set, your first priority should be to review the entire presentation and get a sense of what the data relates to. There is no need to dig into the details at first, you merely want to know whether, for example, this is a retailor reviewing its sales information or a graph describing larger economic trends. You should focus on the organizational features of the data presentation itself. Look for any information that clarifies the data, such as units of measurement or magnitude, graph axes and scales, or any explanatory notes. Any graphical information presented will be drawn to scale.
Once you have a basic understanding of the presented data, then it is time to begin solving the problems. Each question must be answered using only common facts, mathematical knowledge, and the data that has been presented. Even if you know more information about a topic, you should restrict yourself to the data that is presented.
As with the rest of the GRE, Data Interpretation sets will pose problems that are either multiple choice, numeric entry, or quantitative comparison. Quantitative Comparison questions require students to assess which of two quantities is larger, whether they are equal, or whether their relationship cannot be determined from the available information. Multiple choice questions require students to select one or more answers from a list of multiple choices, and Numeric Entry questions require students to write the correct answer in a text box.
Generally, the GMAT is considered to have more difficult Quantitative Reasoning sections than the GRE, and the GRE is considered more challenging in its verbal sections. While the GMAT is intended to assess ability to perform complex analysis in a business context, the GRE is a more general assessments designed to reflect accomplishment over a wide variety of undergraduate coursework. Performing well on the GMAT requires a specific skillset; performing well on the GRE requires a broad skillset combined with specific understanding of how to approach the test.
The Quantitative Reasoning sections of the GRE are organized very differently from comparable sections of the GMAT. While some GRE questions use Data Interpretation Sets to require students to use the same data to solve multiple problems, all GMAT questions are individually timed and do not relate to each other. This distinction also affects the composition of the exam itself.
Both the GMAT and the GRE use computer adaptation to customize each test according to the individual student's performance. The GRE performs this adaptation between test sections – after you complete your first Quantitative Reasoning section, the computer testing program will use your score on the first section to determine the difficulty level of the problems on the second section, assembling a unique problem set from a large pool of possible test questions. The GMAT, on the other hand, adapts after every single question, becoming progressively more difficult if you get answers correctly and progressively easier if you answer questions incorrectly.