GMAT Focus Table Analysis Questions – The Ultimate Guide
Table Analysis is a Data Insights question type on the GMAT: you get one sortable table and three true/false or yes/no statements, and you need all three correct to get...
Table Analysis is a Data Insights question type on the GMAT: you get one sortable table and three true/false or yes/no statements, and you need all three correct to get credit. The winning approach is Scan the table, Sort the column the question depends on, then Solve using estimation rather than exact math. Master those three moves and Table Analysis becomes one of the more predictable question types on the exam.
Table Analysis questions can feel like a treasure hunt through rows and columns of data, but the format rewards a specific, learnable process more than raw math speed. Get the process right, and these questions stop being the section you dread.
This guide covers what Table Analysis actually tests, the types of questions you’ll see, the skills the format rewards, and a full worked example using the same sort-then-solve method you’ll use on test day.
Practise Data Insights questions, Table Analysis included, with instant feedback on Prepathon.
What Are Table Analysis Questions? And why they matter
A sortable table, a split screen, and three statements that all have to be right.
Table Analysis questions sit inside the Data Insights section of the GMAT. Each one hands you a table, sales figures, inventory counts, statistical data, whatever the scenario calls for, and asks you to extract and process that information to evaluate a set of statements.
Every Table Analysis table lets you sort any column, in ascending or descending order. That’s not a convenience feature, it’s the whole strategy. Sorting groups similar values together and surfaces extremes instantly.
| Region | Q1 Sales ($) | Q2 Sales ($) | Total Sales ($) |
|---|---|---|---|
| North | 20,000 | 25,000 | 45,000 |
| South | 22,000 | 30,000 | 52,000 |
| East | 15,000 | 17,000 | 32,000 |
| West | 18,000 | 20,000 | 38,000 |
Sort “Q1 Sales” ascending and East’s 15,000 rises straight to the top. Sort “Total Sales” descending and South’s 52,000 leads. Sorting turns a search problem into a read-off-the-top problem.
The screen splits into two halves. One side holds the scenario description, the sort control, and the table. The other holds three statements, each requiring a bi-polar choice, True/False or Yes/No, depending on the prompt.
You must get all three statements correct to receive credit for the question. Two out of three counts the same as zero out of three.
Three Types of Table Analysis Questions and what each one is really asking
Every Table Analysis statement falls into one of three buckets.
The Three Skills Table Analysis is really testing
Analytical reading, numerical fluency, and fast critical thinking.
Start by reading every column heading carefully. Misreading what a column represents is the single most common source of Table Analysis errors.
You’ll regularly need mean, median, mode, and standard deviation. The GMAT expects estimation and ballparking over exact arithmetic.
Judge the relevance of each data point to the specific statement in front of you. Regular exposure to varied table formats builds the pattern recognition that makes new tables feel familiar.
Prepathon’s GMAT Data Insights tool covers Table Analysis, Graphics Interpretation, and Multi-Source Reasoning.
The SSS Approach — worked example
Scan, Sort, Solve — applied to a real three-statement question.
Work out what kind of information sits in each row and column.
Pay close attention to the column each statement depends on, and sort it.
Estimate and ballpark rather than calculating exactly wherever the statement allows it.
The table lists data on 22 earthquakes of magnitude 7 or greater during a recent year. Time is given in hours, minutes, and seconds (GMT). Latitude is positive north of the equator and negative south of it; longitude is positive east of Greenwich and negative west of it.
| Date | Time (GMT) | Magnitude | Depth (km) | Latitude | Longitude |
|---|---|---|---|---|---|
| 01/03 | 22:36:28 | 7.1 | 25 | -8.799 | 157.346 |
| 01/12 | 21:53:10 | 7.0 | 13 | 18.443 | -72.571 |
| 02/26 | 20:31:27 | 7.0 | 25 | 25.930 | 128.425 |
| 02/27 | 06:34:12 | 8.8 | 23 | -36.122 | -72.898 |
| 04/04 | 22:40:43 | 7.2 | 4 | 32.297 | -115.278 |
| 04/06 | 22:15:02 | 7.8 | 31 | 3.383 | 97.048 |
| 05/09 | 05:59:42 | 7.2 | 38 | 3.748 | 96.018 |
| 05/27 | 17:14:47 | 7.1 | 31 | -13.698 | 166.643 |
| 06/12 | 19:26:50 | 7.5 | 35 | 7.881 | 91.936 |
| 06/16 | 03:16:28 | 7.0 | 18 | -2.174 | 136.543 |
| 07/18 | 13:34:59 | 7.3 | 35 | -5.931 | 150.509 |
| 07/23 | 22:08:11 | 7.6 | 607 | 6.718 | 123.409 |
| 07/23 | 22:51:12 | 7.4 | 586 | 6.486 | 123.467 |
| 07/23 | 23:15:10 | 7.4 | 641 | 6.776 | 123.259 |
| 08/04 | 22:01:44 | 7.0 | 44 | 5.746 | 150.765 |
| 08/10 | 05:23:45 | 7.3 | 25 | -17.541 | 168.069 |
| 08/12 | 11:53:16 | 7.1 | 207 | -1.266 | 77.306 |
| 09/03 | 16:35:46 | 7.0 | 12 | -43.522 | 171.658 |
| 09/29 | 17:11:26 | 7.0 | 26 | -4.963 | 133.086 |
| 10/25 | 14:42:22 | 7.8 | 20 | -3.487 | 100.782 |
| 12/21 | 17:19:41 | 7.4 | 14 | -26.901 | 143.698 |
| 12/25 | 13:16:37 | 7.3 | 16 | 19.702 | 167.947 |
For each statement, select Yes if it’s true based on the data, otherwise No.
- The arithmetic mean of the depths is greater than the median of the depths.
- More than half of the 22 earthquakes occurred north of the equator.
- Exactly half of the earthquakes occurred between 10:00:00 and 20:00:00 GMT.
Sort by Depth. With 22 values, the median is the average of the 11th and 12th positions once sorted, here 25 and 26, giving a median of 25.5. Three depth values (607, 586, 641 km) are wildly larger than the rest. A dataset with even one disproportionately large value will always have a mean well above its median. Answer: Yes.
Sort by Latitude and count how many values are positive (north). Fewer than 11 of the 22 rows carry a positive latitude. The earthquakes north of the equator are a minority, not a majority. Answer: No.
Sort by Time (GMT) and count how many timestamps fall in that window. The count doesn’t land on exactly 11 of 22. It’s short of half. Answer: No.
See how our live GMAT classes drill these patterns until they are automatic under time pressure.
Where Table Analysis Scores Actually Get Lost
Three recurring errors, and the time-management habit that prevents the fourth.
Misunderstanding what a column represents cascades into every statement that touches it. Read headings before anything else.
Skimming instead of reading the full table means missing an outlier or a relevant row entirely.
Every statement must be checked strictly against what the table shows, not against what seems plausible.
Budget roughly two minutes per question. If a table is taking too long, sort and estimate rather than reading every cell.
Recommended Resources for Table Analysis practice
The Official Guide and GMAT Club forums are good supplements, then lean on structured coaching to close the gaps they can’t.
- The GMAT Official Guide 2026–2027 — the complete Official Guide with practice questions across all three sections.
- The GMAT Official Guide Data Insights Review 2026–2027 — 275+ Data Insights questions not in the main Official Guide.
- The GMAT Official Starter Kit + Practice Exams 1 & 2 — free introductory materials and two full-length practice tests.
- GMAT Club — an active community with practice questions and discussion forums.
For structured coaching on Data Insights and every other section, explore Crackverbal’s GMAT Live Online, GMAT Personal Tutoring, and GMAT Fast Track programs.
Frequently Asked Questions: GMAT Table Analysis
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Talk to a GMAT ExpertSort Smarter, Not Faster. That’s the Whole Game.
Mastering Table Analysis isn’t about reading faster. It’s about sorting smarter and estimating instead of calculating. Watch for the two habits that cost the most marks: skimming past a column heading, and drawing a conclusion the data doesn’t actually support. Fix those two, and consistent, confident scores on this question type follow.
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Devmitra Sen is Head of Academics at Crackverbal and has trained over 4,000 students. Her scorers tell the story: GMAT 745, 725, 715, 705 alongside turnarounds like 575→715 and 375→675. She has produced multiple Q90 scores, including a perfect 100th percentile on GMAT Quant — a benchmark very few coaches can claim consistently. On Data Insights, her superpower is changing how students see, observe, and comprehend data: breaking it down, reasoning through it, and zeroing in on exactly what the question asks. The results follow: multiple 90+ percentile DI scores, including a 1st to 99th percentile turnaround in under two and a half months. She carries a quiet interest in the history of mathematical thought — particularly ideas rooted in India long before they were formalised elsewhere — a perspective that gives her an unusually grounded sense of why the subject matters.
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