Graduate Management Admission Test (GMAT) Data Insights Syllabus

Data Insights syllabus — full chapter and topic list

Expand any chapter to see its topics and sub-topics. This is the whole examinable outline for Data Insights in Graduate Management Admission Test (GMAT), not a summary of it.

1. 1

   Data Sufficiency

   3 topics

   • Data Sufficiency Mechanics
     • The five fixed answer choices
     • Sufficiency vs solving for a value
   • Value vs Yes/No Questions
     • Determining a unique value
     • Definitive yes or no determination
   • Statement Evaluation Process
     • Evaluating statements independently first
     • Avoiding the carry-over trap
     • Combining statements only when needed
2. 2

   Multi-Source Reasoning

   3 topics

   • Navigating Tabbed Sources
     • Synthesising text, tables and charts across tabs
   • Inference Across Sources
     • Cross-referencing data points
   • Yes/No and Multiple-Choice Formats
3. 3

   Table Analysis

   3 topics

   • Sorting and Filtering Data
     • Using the sortable column feature
   • Statistical Reading of Tables
     • Identifying medians, ranges and outliers
   • True/False Statement Evaluation
4. 4

   Graphics Interpretation

   3 topics

   • Reading Chart Types
     • Scatter plots and trend lines
     • Bar, line and pie charts
     • Bubble and stacked graphs
   • Drop-down Completion Format
     • Selecting values to complete statements
   • Estimation from Visual Data
5. 5

   Two-Part Analysis

   3 topics

   • Quantitative Two-Part Problems
     • Solving paired equations or constraints
   • Verbal Two-Part Problems
     • Trade-off and conditional reasoning
   • Column-Based Answer Selection

Data Insights flashcards for Graduate Management Admission Test (GMAT)

21 of 50 cards from the Data Insights deck — real questions with worked answers.

1. In GMAT Data Insights Data Sufficiency, what are the five fixed answer choices and what does each mean?

   (A) Statement (1) ALONE is sufficient, but (2) alone is not; (B) Statement (2) ALONE is sufficient, but (1) alone is not; (C) BOTH together are sufficient, but NEITHER alone is sufficient; (D) EACH statement ALONE is sufficient; (E) Together they are still NOT sufficient (more data needed).

2. What is the core task in a Data Sufficiency question (as opposed to Problem Solving)?

   You do not need to compute the actual answer. You only decide whether the given statements provide ENOUGH information to answer the question uniquely. Determine sufficiency, not the value itself.

3. Describe the recommended order for evaluating the two statements in Data Sufficiency.

   Evaluate Statement (1) alone first (ignoring (2)). Then evaluate Statement (2) alone (ignoring (1)). Only if neither alone is sufficient do you combine them. This avoids carrying information illegitimately between statements.

4. In Data Sufficiency, what is the difference between a 'Value' question and a 'Yes/No' question?

   A Value question asks for a specific quantity (e.g., 'What is $x$?') and is sufficient only when a single unique value is forced. A Yes/No question asks whether a condition holds and is sufficient when the answer is a definite 'always yes' OR a definite 'always no'.

5. For a Data Sufficiency Value question, when is a statement sufficient?

   It is sufficient if and only if it pins down exactly one possible value. If two or more different values remain consistent with the statement, it is NOT sufficient.

6. For a Yes/No Data Sufficiency question, why is a consistent 'No' just as sufficient as a consistent 'Yes'?

   Sufficiency means the question can be answered definitively. A statement that always yields 'No' answers the question just as fully as one that always yields 'Yes'. Insufficiency occurs only when the answer could be both yes and no (sometimes yes, sometimes no).

7. In Yes/No Data Sufficiency, what makes a statement INSUFFICIENT?

   The statement is insufficient if it permits at least one case answering 'Yes' and at least one case answering 'No' — i.e., the answer is not uniquely determined.

8. What common trap does the answer choice (C) represent in Data Sufficiency, and how do you avoid choosing it incorrectly?

   (C) means both statements are needed together and neither works alone. The trap is picking (C) when one statement actually suffices alone (answer should be (A), (B), or (D)). Always fully test each statement in isolation before combining.

9. In Data Sufficiency, what does selecting answer (E) imply about combining the statements?

   (E) means that even using both statements together, the question still cannot be answered uniquely — additional information beyond both statements would be required.

10. When testing a Data Sufficiency statement, why is it useful to try to find two contradictory cases?

    If you can find two valid scenarios that satisfy the statement but give different answers (different values, or one yes and one no), you have proven insufficiency. Finding only one consistent answer across all valid cases proves sufficiency.

11. In the Multi-Source Reasoning (Navigating Tabbed Sources) question type, how is information presented?

    Information is split across two or three tabbed pages (e.g., emails, tables, charts, text). You click between tabs to gather data, then answer questions that often require synthesizing information from more than one tab.

12. What is the most important reading strategy for Multi-Source Reasoning tabbed prompts?

    Do not memorize every detail upfront. Skim each tab to learn what kind of information it holds, then return to specific tabs as each question demands. Let the questions drive which tab(s) you read in depth.

13. In Multi-Source Reasoning, what does an 'Inference Across Sources' question require you to do?

    Combine facts located in different tabs to draw a conclusion that no single source states explicitly. You must cross-reference data and apply logical reasoning rather than locating a single stated fact.

14. What two answer formats appear in Multi-Source Reasoning, and how does each work?

    (1) Standard five-option multiple choice (pick one). (2) A three-statement table where each row is judged independently, typically as Yes/No, True/False, or 'Inferable/Not inferable' — all three rows must be correct for credit.

15. In a Multi-Source Reasoning Yes/No (three-statement) question, how is partial credit awarded?

    There is no partial credit. You must correctly classify all three statements (e.g., all three Yes/No judgments) to earn the point.

16. In the Table Analysis question type, what interactive feature is central to answering, and how do you use it?

    A sortable table: you can sort by any column (ascending/descending) using a drop-down. Sorting groups or orders rows so you can quickly find maxima, minima, medians, or filter patterns relevant to the question.

17. How does sorting a column help answer Table Analysis questions about extremes or order statistics?

    Sorting ascending puts the smallest at top and largest at bottom (and vice versa), making the minimum, maximum, and the middle (median) value immediately visible without scanning all rows manually.

18. In Table Analysis, how do you find the median of a column with $n$ entries after sorting?

    Sort the column. If $n$ is odd, the median is the middle entry at position $\frac{n+1}{2}$. If $n$ is even, the median is the average of the two middle entries at positions $\frac{n}{2}$ and $\frac{n}{2}+1$.

19. In Table Analysis statements judged True/False, what proves a statement FALSE?

    A single counterexample row in the table that violates the claim makes the statement False. A statement is True only if it holds for every relevant row the claim covers.

20. What statistical measures should you be ready to read or compute from a Table Analysis dataset?

    Mean (average), median, mode, range, and proportions/percentages, plus relationships like correlation direction between two columns. You may also need to count rows meeting a condition.

21. How do you compute the mean of a data column with values $x_1,\dots,x_n$?

    $$\bar{x}=\frac{1}{n}\sum_{i=1}^{n} x_i = \frac{x_1+x_2+\cdots+x_n}{n}$$

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