OWNING THE DATASET
Let's start by understanding what we're working with. This table shows data for 21 airports, comparing passenger numbers and aircraft movements (takeoffs and landings) with percentage changes from the previous period.
Key insights from our initial dataset review:
- We have both raw rankings and percentage changes for two metrics
- The table includes airports from multiple countries, including several from the USA
- Both metrics show a mix of increases (positive %) and decreases (negative %)
- The percentage changes range from quite negative (Chicago at \(-9.0\%\) for passengers) to substantially positive values
Quick scan strategy: Rather than analyzing every number, let's sort the data as needed for each statement. This will instantly reveal patterns and save us significant effort.
ANALYZING STATEMENT 1
Statement 1 Translation:
Original: "The airport with the greatest decrease in passengers also had the greatest decrease in movements."
What we're looking for:
- Identify which airport had the largest passenger % decrease
- Check if that same airport also had the largest movement % decrease
In other words: Does the airport with the most negative passenger change also have the most negative movement change?
Let's approach this efficiently by sorting:
First, let's sort the passenger % change column in ascending order (smallest/most negative values at top)
After sorting, we immediately see Chicago has the greatest passenger decrease at \(-9.0\%\)
Now we need to check: Does Chicago also have the greatest movement decrease?
Chicago's movement change is \(-4.9\%\)
Let's sort the movement % change column in ascending order
After sorting, we see Los Angeles has a movement decrease of \(-8.6\%\), which is a greater decrease than Chicago's \(-4.9\%\)
Therefore, Statement 1 is False.
Teaching note: Notice how sorting instantly revealed the key airports without manually scanning all 21 entries. Once we found an airport with a greater movement decrease than Chicago, we could immediately stop our analysis.
ANALYZING STATEMENT 2
Statement 2 Translation:
Original: "The airport with the median passenger rank is the same as the airport with the median movement rank."
What we're looking for:
- Find the airport with the median (middle) passenger rank
- Find the airport with the median movement rank
- Check if they're the same airport
In other words: Is the middle-ranked airport for passengers also the middle-ranked airport for movements?
With 21 airports, the median position is the 11th position (10 above, 10 below):
Let's sort by passenger rank in ascending order
After sorting, the 11th position (median) shows Madrid with passenger rank 11
Now let's sort by movement rank in ascending order
After sorting, the 11th position (median) shows Frankfurt with movement rank 12
Since Madrid and Frankfurt are different airports, Statement 2 is False.
Teaching note: Sorting makes finding the median a visual identification task - simply count to the middle position. No need to write out all 21 ranks!
ANALYZING STATEMENT 3
Statement 3 Translation:
Original: "Exactly 50% of the airports that had increases in both passengers and movements are located in the USA."
What we're looking for:
- Identify airports with increases in BOTH metrics (both percentages positive)
- Count how many of these are in the USA
- Calculate if USA airports are exactly 50% of this group
In other words: Of airports with positive changes in both columns, is exactly half the count from the USA?
Let's use sorting to efficiently find our answer:
Sort by passenger % change in descending order (puts positive values at the top)
Now we can scan down the sorted list and check each airport with positive passenger change:
- For each airport with positive passenger %, check if its movement % is also positive
- Keep two counts: total airports with dual increases and how many of these are in the USA
After scanning, we find:
- Total airports with increases in both metrics: 6
- USA airports with increases in both metrics: 3
That gives us \(\frac{3}{6} = 50\%\) exactly
Therefore, Statement 3 is True.
Teaching note: Sorting created a natural stopping point - once we hit negative passenger values, we knew we'd found all relevant airports. This eliminated the need to check all 21 airports individually.
FINAL ANSWER COMPILATION
After analyzing all three statements:
- Statement 1: False
- Statement 2: False
- Statement 3: True
LEARNING SUMMARY
Skills We Used
- Strategic Sorting: Every statement was solved by sorting first, which transformed complex comparisons into simple visual identification
- Targeted Verification: For Statement 1, we only needed to check if Chicago had the maximum movement decrease, not identify every decrease
- Pattern Recognition: Sorting created visual patterns that made answers immediately apparent
- Natural Stopping Points: For Statement 3, we only needed to check airports with positive passenger changes, not all 21 airports
Strategic Insights
- Sort First Approach: Always consider sorting as your first analysis technique for table problems
- Median Shortcut: When finding medians, sort and identify the middle position rather than counting all values
- Counter-Example Efficiency: For "all" or "greatest" statements, you only need one counter-example to disprove the claim
- Two-Stage Filtering: For Statement 3, we filtered for one condition, then applied the second condition to that filtered set
Common Mistakes We Avoided
- Manually scanning all 21 airports multiple times for each statement
- Writing out and counting all values to find medians
- Performing calculations when simple comparisons were sufficient
- Checking all airports when sorting created natural boundaries
Remember: GMAT Table Analysis problems reward strategic sorting and visual pattern recognition. Almost every question can be drastically simplified through appropriate column sorting rather than manual comparison or calculation.
