What You'll Learn This Hour
- 1How to read and navigate a GMAT Table Analysis question, including understanding column headers and data relationships.
- 2The four-step sorting strategy: identify the relevant column, mentally rank rows, verify the exact claim, and mark True or False independently.
- 3Common GMAT traps in Table Analysis — including "all vs. most," absolute vs. relative comparisons, and compound conditions.
- 4How to compute derived columns (e.g., GDP per capita, density) on the fly and use them to answer quantitative True/False statements quickly and accurately.
Core Concepts: Table Analysis
What Is Table Analysis?
Table Analysis presents a sortable spreadsheet-style table with several rows and columns of data. You are given 5–6 True/False (or Yes/No) statements to evaluate. Each statement is graded independently — getting one wrong does not affect others.
The Golden Rule
Every True/False statement can be verified by sorting or filtering the table by one or two columns. Never estimate — always confirm with exact values. The table is sortable on the real GMAT, so always sort by the column most relevant to the claim being tested.
The 4-Step Table Analysis Strategy
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1
Read ALL column headers first. Before looking at any statement, understand what data is in the table and what units are used. Missing a unit (thousands vs. millions) is a classic trap.
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2
Identify what each statement is really asking. Is it asking for a maximum? A minimum? A comparison between two rows? A calculation using two columns? Underline the key word.
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3
Mentally sort by the relevant column. On the real GMAT you can click a column header to sort. In your head, find the highest or lowest value in the column the statement references.
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4
Verify — do not assume. After identifying the candidate row(s), read the exact values. Check whether they satisfy the statement as written. Mark True or False.
Visual: Sorting in Action
The table below shows five companies with Revenue, Employees, and Revenue per Employee. The highlighted row (Gamma) has the highest Revenue/Employee. Sort by that column to answer Q3 instantly.
Sort by Rev/Employee to answer Q3: "Which company has the highest revenue per employee?" → Gamma (260K)
Worked Examples
Reference Table — Six Cities
| City | Population (M) | GDP ($B) | GDP/Capita ($K) | Area (km²) | Density (per km²) |
|---|---|---|---|---|---|
| Aldor | 3.2 | 128 | 40.0 | 820 | 3,902 |
| Breva | 1.8 | 90 | 50.0 | 310 | 5,806 |
| Colta | 5.5 | 165 | 30.0 | 1,250 | 4,400 |
| Drenk | 2.4 | 96 | 40.0 | 600 | 4,000 |
| Elvar | 4.1 | 205 | 50.0 | 980 | 4,184 |
| Forin | 0.9 | 36 | 40.0 | 150 | 6,000 |
"The city with the largest population also has the largest GDP."
Step-by-Step Solution:
Step 1 — Sort by Population: Colta = 5.5M (largest), Elvar = 4.1M, Aldor = 3.2M...
Step 2 — Note Colta's GDP: Colta GDP = $165B
Step 3 — Sort by GDP: Elvar = $205B (largest), Colta = $165B, Aldor = $128B...
Step 4 — Compare: Largest population = Colta. Largest GDP = Elvar. These are different cities.
Answer: FALSE — Colta has the largest population, but Elvar has the largest GDP ($205B vs $165B).
"Forin has a higher population density than Colta."
Step-by-Step Solution:
Step 1 — Find Density values directly from table: Forin Density = 6,000/km². Colta Density = 4,400/km².
Step 2 — Verify with formula (optional): Forin = 0.9M / 150 km² = 6,000. Colta = 5.5M / 1,250 km² = 4,400. Both match.
Step 3 — Compare: 6,000 > 4,400. Yes, Forin is denser.
Answer: TRUE — Forin's density (6,000/km²) exceeds Colta's (4,400/km²), despite Forin being much smaller in population.
"More than half of the cities have a GDP per capita of at least $40,000."
Step-by-Step Solution:
Step 1 — Identify the threshold: $40,000 = $40K GDP per capita. Column is already in $K.
Step 2 — Check each city: Aldor = 40 (meets), Breva = 50 (meets), Colta = 30 (does not), Drenk = 40 (meets), Elvar = 50 (meets), Forin = 40 (meets).
Step 3 — Count: Cities meeting threshold = Aldor, Breva, Drenk, Elvar, Forin = 5 out of 6.
Step 4 — Evaluate "more than half": More than half of 6 = more than 3. We have 5. Yes.
Answer: TRUE — 5 of 6 cities (83%) have GDP per capita of at least $40K, which is more than half.
GMAT Traps to Avoid
Trap 1: "All" vs. "Most"
A statement using "all" is false if even one exception exists. "Most" requires more than 50%. Always count carefully instead of eyeballing.
Trap 2: Each Statement Is Independent
True/False items are graded separately. A wrong answer on Statement 2 does NOT mean Statement 3 is also wrong. Evaluate every statement from scratch using the table.
Trap 3: Absolute vs. Relative
A city can have the highest total GDP but the lowest GDP per capita. A company can have the most employees but the least revenue per employee. Never confuse absolute size with ratios.
Trap 4: Units and Scale
Column headers may say "$B" or "$K" or "per 1,000." Misreading a unit turns a correct calculation into a wrong answer. Always re-read the header before computing.
Trap 5: "Cannot Be Determined" Rarely Applies
Unlike some other DI question types, Table Analysis statements are specifically designed so that the table contains enough data to determine True or False. If you think the data is insufficient, re-read the table — you likely missed a column or misread a number.
Practice Questions
Two Table Analysis sets, each with 6 True/False statements. Use the reference table above (Six Cities data). Click "Show Answer" for a full explanation.
Statement 1
"Elvar has both the highest GDP and the highest GDP per capita among all six cities."
Show Answer
TRUE. Sort by GDP: Elvar = $205B (highest). Sort by GDP/Capita: Elvar = $50K and Breva = $50K. Wait — they tie! But the statement says "highest," which includes being tied for the top. Elvar does have a GDP/Capita of $50K, which is the maximum value in that column (shared with Breva). Since the statement is about Elvar specifically having "the highest" — and Elvar does have the highest GDP ($205B vs. Breva's $90B), and ties for highest GDP per capita — the statement is TRUE for GDP. For GDP/Capita, Elvar and Breva both = $50K. Elvar is among the highest. The statement holds: TRUE.
Statement 2
"Colta has a larger GDP than Aldor."
Show Answer
TRUE. Colta GDP = $165B. Aldor GDP = $128B. 165 > 128. The statement is correct.
Statement 3
"The city with the smallest area also has the smallest population."
Show Answer
TRUE. Sort by Area: Forin = 150 km² (smallest). Sort by Population: Forin = 0.9M (smallest). Both minimums belong to Forin. The statement is correct.
Statement 4
"Breva has a higher GDP per capita than Aldor."
Show Answer
TRUE. Breva GDP/Capita = $50K. Aldor GDP/Capita = $40K. 50 > 40. Even though Breva's total GDP ($90B) is less than Aldor's ($128B), its per-capita figure is higher. This tests your ability to distinguish absolute vs. per-capita values.
Statement 5
"All six cities have a population density greater than 3,500 people per km²."
Show Answer
TRUE. Check all density values: Aldor = 3,902, Breva = 5,806, Colta = 4,400, Drenk = 4,000, Elvar = 4,184, Forin = 6,000. The minimum is Aldor at 3,902, which exceeds 3,500. All six cities satisfy the condition. The statement is TRUE.
Statement 6
"The combined GDP of Aldor and Forin is greater than the GDP of Colta."
Show Answer
TRUE. Aldor GDP = $128B. Forin GDP = $36B. Combined = $164B. Colta GDP = $165B. Wait — 164 < 165. This is FALSE, not TRUE! The combined GDP of Aldor and Forin ($164B) is actually slightly LESS than Colta's ($165B).
CORRECT Answer: FALSE — $128B + $36B = $164B, which is 1 billion less than Colta's $165B. A close one — never estimate, always add precisely.
Statement 7
"Exactly two cities have a GDP per capita above $45,000."
Show Answer
TRUE. GDP/Capita values: Aldor = 40, Breva = 50, Colta = 30, Drenk = 40, Elvar = 50, Forin = 40. Values above $45K (i.e., above 45): Breva = 50 and Elvar = 50. That is exactly 2 cities. TRUE.
Statement 8
"Drenk has a higher population density than Aldor."
Show Answer
TRUE. Drenk density = 4,000/km². Aldor density = 3,902/km². 4,000 > 3,902. The difference is small, so you must read the exact numbers. Drenk is denser. TRUE.
Statement 9
"The city with the highest GDP per capita has more than 3 million residents."
Show Answer
FALSE. Highest GDP/Capita = $50K, shared by Breva (1.8M residents) and Elvar (4.1M residents). Breva has only 1.8M, which is less than 3M. Since there are two cities tied for the highest GDP/Capita, and one of them (Breva) has fewer than 3M residents, the statement is not universally true. FALSE.
Statement 10
"Among cities with a population density above 5,000 per km², the total GDP exceeds $100 billion."
Show Answer
TRUE. Filter by Density > 5,000: Breva = 5,806 and Forin = 6,000. Their GDPs: Breva = $90B, Forin = $36B. Total = $90B + $36B = $126B. $126B > $100B. TRUE.
Statement 11
"Colta has both the largest population and the largest area among all six cities."
Show Answer
TRUE. Sort by Population: Colta = 5.5M (largest). Sort by Area: Colta = 1,250 km² (largest). Colta tops both columns. The statement requires BOTH conditions to hold simultaneously, and both do. TRUE.
Statement 12
"The city with the lowest GDP per capita has a population density below 4,000 per km²."
Show Answer
TRUE. Lowest GDP/Capita = Colta at $30K. Colta's density = 4,400/km². Wait — 4,400 is NOT below 4,000. This is FALSE! Colta's density (4,400) exceeds 4,000, so the statement is FALSE.
CORRECT Answer: FALSE — Colta has the lowest GDP/Capita ($30K) but its density is 4,400/km², which is above 4,000. The statement is false. This is a classic trap where a plausible-sounding link between two columns turns out to be wrong.
Quick Reference Card
# TABLE ANALYSIS — KEY RULES & FORMULAS
# Derived column formulas (compute on the fly)
GDP per Capita = GDP / Population
Density = Population / Area
Revenue/Employee = Revenue / Employees
Growth Rate = (New - Old) / Old * 100
# Sorting shortcuts
MAX of column → scan for largest value
MIN of column → scan for smallest value
Top N values → sort descending, count N
Condition count → filter, then tally rows
# Quantifier thresholds
"more than half" → count > n/2
"at least X%" → verify each row individually
"all" → one counterexample = FALSE
"none" → one match = FALSE
# 4-step method (30 sec per statement)
1. Read headers & units
2. Parse the claim (what column? what threshold?)
3. Sort / filter mentally
4. Verify exact values → mark T or F
# Time target
Table Analysis set → ~2.5 minutes total
Per statement → ~25–30 seconds