GMAT · Table Analysis · TOPIC LESSON
Preserve row identities, calculate the right denominator and test each claim separately.
BY THE END, YOU CAN
BEFORE YOU START
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01 / UNDERSTAND & APPLY
Table Analysis asks you to evaluate claims against a structured dataset. First identify what each row represents and which columns are counts, rates, times or categories. A value such as 20 is uninterpretable without its header.
Fictional production table: columns are team, January units, February units, February operating hours, and February defective units. Rows: A: 80,100,20,2; B: 100,90,15,0; C: 60,90,18,3; D: 120,150,30,6; E: 40,60,12,not recorded; F: 100,110,22,1. Units produced include defective units. A blank defect entry means unknown, not zero.
The figure is an original static representation of a sortable table. These lessons show the reasoning behind sorting; they do not require an interactive sorting widget.
Full-size scientific diagram. Swipe or scroll horizontally; keyboard users can focus the diagram and use arrow keys.
ORIGINAL WORKED EXAMPLE
Production table: What is Team A’s February gross output per operating hour?
Answer 5 units/hour
02 / UNDERSTAND & APPLY
Sorting a table by February units changes the order of rows, not which team owns each value. Sorting only one numeric column would corrupt the data by attaching it to the wrong team. Ties must remain visible.
Ascending February order is E(60), B(90), C(90), A(100), F(110), D(150), with B and C interchangeable within the tie. A rank question must specify how ties are handled; a count question simply counts records satisfying its condition.
ORIGINAL WORKED EXAMPLE
Production table: How many teams produced at least 100 February units, and which team had the third-largest distinct February output? Give both answers.
Answer Count: 3; team: A
ORIGINAL PRACTICE · 1
B and C
Both rows contain 90 in the February units column. Sorting does not break or eliminate the tie.
03 / UNDERSTAND & APPLY
A multiple-condition filter is an intersection for AND and a union for inclusive OR. Check each condition against the same row. A row satisfying only one condition fails an AND filter.
Do not confuse “at least” with “more than.” Threshold equality matters, especially when a table contains observations exactly at the boundary.
ORIGINAL WORKED EXAMPLE
Production table: Which teams have February output at least 90 and February hours at most 20?
Answer A, B and C
ORIGINAL PRACTICE · 1
2
C improves 60→90 with 18 hours and E improves 40→60 with 12 hours. B has 15 hours but declines; the other teams exceed 18 hours.
ORIGINAL PRACTICE · 2
2
E qualifies through output 60. D qualifies through hours 30. No other row meets either condition; count each qualifying row only once.
04 / UNDERSTAND & APPLY
For an even number of observations, the median is the average of the two central values after sorting. Sorting by a different column does not automatically order the target variable. Missing values also require a stated treatment rather than an invented zero.
The median is robust to some extreme-value changes but still depends on positions. The mean uses every value and can move when an extreme value changes even if the median stays fixed.
ORIGINAL WORKED EXAMPLE
Production table: Find the median and mean February output.
Answer Median: 95 units; mean: 100 units
ORIGINAL PRACTICE · 1
19 hours
Sorted times are 12,15,18,20,22,30. The middle pair is 18 and 20, whose average is 19.
05 / UNDERSTAND & APPLY
Percent change=(new−old)/old×100%. A percentage increase measured against January uses January as denominator, not February. A defective-unit rate uses defective units divided by the relevant total units, not operating hours.
Equal absolute increases can have different percentage changes. A 20-unit gain on a base of 40 is 50%, while the same gain on a base of 80 is 25%.
ORIGINAL WORKED EXAMPLE
Production table: Do Teams C and E have equal percentage increases from January to February?
Answer Yes, each increases 50%
ORIGINAL PRACTICE · 1
−10%
The change is −10 with January base 100, so −10/100=−10%. Dividing by February’s 90 answers a different comparison.
ORIGINAL PRACTICE · 2
4%
Six defective units out of 150 total gives 6/150=0.04. The 30 operating hours are not the denominator.
06 / UNDERSTAND & APPLY
An “all” claim fails with one counterexample and succeeds only after every relevant row is checked or a valid general argument covers them. A “some” claim needs one witness. A “none” claim fails with one witness.
For each binary row, apply the stated criterion separately. Multiple claims can all be true or all false. There is no prescribed pattern of responses.
ORIGINAL PRACTICE · 1
(i) False; (ii) True; (iii) False
B declined, refuting (i). C and E each gained 50%, supporting (ii). B produced 90/15=6 units/hour, refuting (iii). This is one exercise requiring three row judgments.
07 / UNDERSTAND & APPLY
A missing observation prevents some conclusions but does not automatically make the associated claim false. Use the question’s requested labels: “Supported by the table” can be answered no when evidence is missing, even though the claim could happen to be true.
For the production set, E’s defect count is not recorded. You can calculate E’s gross productivity and growth, but you cannot calculate its nondefective output or its exact defect rate.
ORIGINAL PRACTICE · 1
(i) Not supported; (ii) Supported
Missing E data cannot establish zero. Known counts sum 2+0+3+6+1=12. The overall six-team total is not thereby fixed.
08 / UNDERSTAND & APPLY
Teams with longer operating times tend here to have higher output, but a descriptive pattern does not show that assigning more hours would produce the same effect for every team. Equipment, staffing, demand and other variables may differ.
A final audit checks row identity, units, denominator, missingness and quantifier. This sequence is more reliable than performing every available calculation and then searching for a matching answer.
KEEP THE BIG IDEAS
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