Researchers collected and studied emails sent by staff at three campuses of an Australian university. The table shows the exact...

OWNING THE DATASET

Let's start by understanding what we're working with. This table shows the percentage distribution of emails sent between different campuses.

Key insights about this dataset:

• Each row represents emails from a specific campus (the source)
• Each column shows what percentage went to each campus (the destination)
• Each row totals 100% (all emails from a source must go somewhere)
• We only have percentage distributions, not absolute numbers
• This means we can make certain comparisons but not others

For example, looking at emails from Campus 1:

• 39% went to Campus 2
• 13% went to Campus 3
• 6.5% went to Campus 4
• And so on...

Note: The absence of absolute numbers will be crucial in our approach. We can compare proportional patterns, but can't directly compare total volumes between campuses without additional information.

ANALYZING STATEMENT 1

Statement 1 Translation:
Original: "Were more emails sent to Campus 1 than Campus 2?"
What we're looking for:

• Whether the total volume of emails to Campus 1 exceeds Campus 2
• This requires comparing across multiple sources

In other words: Did Campus 1 receive a higher total number of emails than Campus 2?

Let's approach this strategically. Without absolute numbers, we need to check if the pattern is consistent across all sources.

Rather than calculating everything, let's scan for contradictions - just one contradiction proves this is unanswerable:

From Campus 1:

• To Campus 2: 39%
• To Campus 1: Not applicable (can't email itself)
• This suggests more emails going to Campus 2

From Campus 3:

• To Campus 1: 48%
• To Campus 2: 24%
• This suggests more emails going to Campus 1

We've found our contradiction! From Campus 1, a higher percentage goes to Campus 2, but from Campus 3, a higher percentage goes to Campus 1.

Teaching callout: Notice how we didn't need to check all sources or calculate any totals. Finding just one contradiction is enough to determine we can't answer the question without absolute numbers.

Statement 1 answer: Cannot be determined

ANALYZING STATEMENT 2

Statement 2 Translation:
Original: "Were more emails sent to Campus 3 than Campus 4?"
What we're looking for:

• Whether total volume of emails to Campus 3 exceeds Campus 4
• Again requires comparing across multiple sources

In other words: Did Campus 3 receive a higher total number of emails than Campus 4?

Let's apply visual pattern checking. We'll scan for the relationship between Campus 3 and Campus 4 across all sources to see if it's consistent:

From Campus 1:

• To Campus 3: 13%
• To Campus 4: 6.5%
• \(\mathrm{Campus\ 3} > \mathrm{Campus\ 4}\) ✓

From Campus 2:

• To Campus 3: 7%
• To Campus 4: 2.3%
• \(\mathrm{Campus\ 3} > \mathrm{Campus\ 4}\) ✓

From Campus 3:

• To Campus 4: 4%
• To Campus 3: Not applicable (can't email itself)
• \(\mathrm{Campus\ 3} > \mathrm{Campus\ 4}\) ✓

The pattern is consistent across all sources - in each case, a higher percentage goes to Campus 3 than to Campus 4. Since this relationship holds true from every source campus, we can definitively answer yes.

Teaching callout: Unlike Statement 1, here we found a consistent pattern across all sources. When every source shows the same relationship, we can reach a definitive conclusion even without absolute numbers.

Statement 2 answer: YES

ANALYZING STATEMENT 3

Statement 3 Translation:
Original: "What percentage of emails to Campus 5 were from Campus 2?"
What we're looking for:

• Of all emails received by Campus 5, what percentage came from Campus 2
• Requires identifying all sources that sent to Campus 5

In other words: Out of 100% of emails that arrived at Campus 5, what portion originated from Campus 2?

Let's use instant pattern recognition to solve this efficiently:

1. Scan the Campus 5 column for all non-zero values
2. We find:
   • From Campus 2: 2.3%
   • From all other campuses: 0%

Since Campus 2 is the only source that sent emails to Campus 5, it accounts for 100% of all emails received by Campus 5.

Teaching callout: This is a great example of visual shortcut power. When only one value exists, we don't need calculations - the answer is automatically 100%. Always scan for zero values first as they can dramatically simplify problems.

Statement 3 answer: 100%

FINAL ANSWER COMPILATION

Statement 1: Cannot be determined
Statement 2: YES
Statement 3: 100%

LEARNING SUMMARY

Skills We Used

• Pattern recognition instead of calculation: We looked for relationships rather than computing specific values
• Contradiction finding: For Statement 1, finding inconsistent patterns quickly gave us the answer
• Consistency checking: For Statement 2, verifying the same relationship across all sources was definitive
• Zero-value insight: For Statement 3, recognizing when only one source contributes made the answer obvious

Strategic Insights

1. Know when you need absolute numbers: Percentage distributions alone can't answer certain comparative questions unless patterns are consistent
2. Look for contradictions first: One contradiction proves a question cannot be determined
3. Check for complete patterns: When all sources show the same relationship, you can reach definitive conclusions
4. Scan for zero values: They often provide immediate shortcuts to answers

Common Mistakes We Avoided

• Attempting to calculate "total emails" when we only have percentages
• Checking every combination when one contradiction is sufficient
• Missing the significance of zero values
• Over-analyzing data when visual pattern recognition is faster

Remember, in GMAT table analysis, strategic pattern recognition will always be more efficient than exhaustive calculations. Train yourself to see relationships rather than computing every value!