GRE Data Interpretation: GRE Data Interpretation Questions, GRE Data Analysis, Tips and Tricks Image
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GRE Data Interpretation: GRE Data Interpretation Questions, GRE Data Analysis, Tips and Tricks

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Yocket Editorial Team
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Unlike the regular data interpretation, GRE Data Interpretation is way different. You need to possess special skills to gain immense success in data analysis. All the quantitative reasoning questions can proficiently measure your data interpretation, problem-solving, arithmetic, algebra, and geometry skills. There are at least 6 questions from data analysis, out of the total 40 in the GRE Quant section.

Meaning that GRE data interpretation holds a good 15% of the entire maths section.

The GRE consists of two quantitative reasoning sections, with 20 questions each. Examinees are required to complete each section within 35 minutes. This portion of GRE Data Analysis questions is scored in one-point increments ranging from 130 to 170. Wish to know more? Keep reading as we guide you through GRE Data Analysis, along with practice questions, and lots of tips!

  Table of Contents

  1. Importance of GRE Data Analysis
  2. GRE Data Analysis Exam Pattern
  3. GRE Data Analysis Syllabus
  4. Types of GRE Data Analysis Question Types
  5. GRE Data Analysis Preparation
  6. GRE Data Analysis Score
  7. Frequently Asked Questions about GRE Data Analysis

Importance of GRE Data Analysis

The GRE Data Analysis significantly determines how you understand and visualise data in a more complex way. In this section of data interpretation, you can score considerably higher to gain admission to the top grad schools. Thus, you can proficiently solve whatever detailed data is provided to you.

  • For the GRE data interpretation, you need not have knowledge of academically complex maths concepts. Only with basic skills and an understanding of data interpretation can you master this portion of the GRE.
  • Data interpretation in GRE is also an effective technique for delivering clear information without using any words. It also helps you to visualise information within a short time span. Nevertheless, there are few chances for errors to occur as well.
  • To score more than 160 in the quantitative section, you must carefully learn and practise GRE data interpretation since you can expect at least 6 to 8 GRE graph questions in each quant section.

Suggested: Importance of Knowing Formulas for GRE Quantitative Section

GRE Data Analysis Exam Pattern

Data analysis is a significant part of the four main concepts of GRE quantitative reasoning. It is the ability to visualise or interpret data, making sense of the information. In GRE Data Analysis practice questions, you’ll find statistics, probability and interpreting graphs.

This quantitative section might have multiple choice or numeric entry questions. Each of the GRE tests comprises twenty questions in the maths section. And only three data interpretation questions are asked in each area. So you get six data interpretation questions in your GRE test.

Suggested: Know Everything about GRE Quantitative 2022!

GRE Data Analysis Syllabus

While you go through the GRE Data Analysis practice questions, you’ll notice it comes in more than a single form. Thus it is ideal for learning GRE data interpretation in GRE formulas thoroughly. It usually presents information in numerous visual formats, including:

  • Bar Charts
  • Pie Charts
  • Histograms
  • Line Graphs
  • Box Plots
  • Best-Fit Lines and Scatter Plots
  • Numerical Data Charts

Suggested: All about the GRE Quantitative Syllabus 2022!

Types of GRE Data Analysis Questions

The four types of questions are asked in Data Analysis in GRE are:

Column Chart

All data is provided through bar or column charts. And to answer the questions, you need to understand the visual information better.

Pie Chart

Here, information is given through pie charts, and you have to answer questions by deriving data.

Box and Scatter Plots

These are basically used for displaying bivariate data. So it would be best if you had the proficiency in studying them and answering all questions.

Line Chart

This data shows a comparison between the progress of the two quantities. So you need to identify all data points clearly to answer the questions.

Suggested: A Complete Guide on GRE Geometry!

GRE Data Analysis Preparation

Here are some Data Interpretation GRE tips and tricks to master in this curriculum:

  • You need to understand the data presentation thoroughly
  • You must understand the measurements
  • You should make use of graphs and review them
  • Keep yourself calm and then answer the questions

Suggested: How to Prepare for GRE Quantitative 2022?

As a part of GRE data interpretation preparation, books recommended are:

  • GRE Quantitative Comparisons & Data Interpretation: It is probably the best book for GRE data interpretation by Manhattan Prep. It consists of the most content-driven materials and new practice problems. Practising this book regularly will effectively cover all portions of the possible quantitative questions. It covers all GRE data analysis practice questions that can help in preparing for the test.
  • Official GRE Super Power Pack: This official GRE preparation book covers all the essential GRE data analysis questions along with solutions. This three book pack resource includes four actual data interpretation GRE tips and tricks, hundreds of original GRE practise questions of verbal, quant, and writing, along with helpful question-answering suggestions.

Suggested: Best Books for GRE Analytical Writing!

GRE Data Analysis Score

Usually, the quantitative reasoning sections of GRE are scored on a scale of 130 to 170 in one-point increments. So 130 is the lowest possible point while 170 is the highest.

Suggested: How to Score High in GRE?

To obtain admission to the world's top institutions, you must score well in Data Analysis in GRE. And in order to make this possible, you should know vital Data Interpretation GRE tips and tricks. For more details on GRE Data Interpretation, why not get in touch with our skilled Yocket Counsellors today?

Frequently Asked Questions about GRE Data Analysis

Ques. What is Data Interpretation in GRE?

Ans. Any question which involves visual information, including charts, graphs, tables, and plots, is known as Data Interpretation. You will get around 6-8 GRE Data Interpretation questions in your exam. This will efficiently test your knowledge of ratios, percentages, and other basic statistical measures, including median, mean, and mode.

Ques. Is GRE Data Interpretation tough?

Ans. The GRE Data Interpretation questions are relatively easy compared to other questions in the Quant section. The whole point of the GRE Data Interpretation is to make information easier to visualise.

Ques. How can I improve in GRE Data Interpretation?

Ans. Following the correct tips, tricks, and strategies will help you improve your GRE Data Interpretation. Some of the best strategies include:

  • Read all information carefully before heading over to answer any questions. If you start reading the questions straight away, it is likely to mislead you.
  • Look at all the graphs, charts, and tables very minutely.
  • Consider taking notes and performing rough work for maths.
  • Thoroughly check for patterns.

Ques. How can I start solving Data Interpretation questions?

Ans. Some tips for efficiently answering any Data Interpretation questions in GRE are:

  • You can start by reviewing the data first.
  • You don’t require a maths degree to understand the provided data.
  • The questions asked have multiple-choice options.
  • Carefully answer all the questions asked.
  • Revise and check options thoroughly before answering.
  • Boost your skills and get faster.

Ques. What are the basics of Data Interpretation in GRE?

Ans. The procedure of data reviewing through specific predefined processes is known as Data Interpretation. It helps deliver meaning to the pre-existing data, thereby arriving at a relevant conclusion. The basics of Data Interpretation in GRE involve data analysis results, inferences on the studied relations, and using them for a conclusion.

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