The table shows the normal and extreme high and low temperatures, and the normal and maximum amounts of precipitation, for...

OWNING THE DATASET

Let's start by understanding our weather data table with the intention of "owning the dataset." This table shows various climate measurements across 12 months for a particular location, with multiple metrics for each month.

Key observations:

• We have temperature data (normal high, normal low, extreme high, extreme low) in degrees Celsius
• We have precipitation data (normal and maximum) in centimeters
• Some months have identical values in certain categories (like Jan/Dec with \(30.5\text{ cm}\) normal precipitation)
• The data shows seasonal patterns with colder temperatures in winter months

Key insight: By immediately looking for patterns and identical values, we can quickly identify potential comparison points that will help us evaluate the statements efficiently.

ANALYZING STATEMENT 1

Statement 1 Translation:
Original: "The rankings of the 12 months from highest to lowest normal precipitation are identical to the rankings from highest to lowest maximum precipitation."
What we're looking for:

• Compare the order of months when sorted by normal precipitation
• Compare the order of months when sorted by maximum precipitation
• Check if these two rankings match exactly

In other words: Do months with more normal precipitation always have more maximum precipitation, in the exact same order?

Let's approach this efficiently. If we find just ONE case where the relative rankings don't match, the statement is false. The fastest way to check this is to look for months with identical normal precipitation but different maximum precipitation.

Sorting approach: First, scan for any tied values in normal precipitation.

Looking at the normal precipitation column, we see January and December both have \(30.5\text{ cm}\).

• January: \(30.5\text{ cm}\) normal precipitation, \(50.8\text{ cm}\) maximum precipitation
• December: \(30.5\text{ cm}\) normal precipitation, \(48.3\text{ cm}\) maximum precipitation

We've found our counterexample! Since January and December tie in normal precipitation but have different maximum precipitation values (\(50.8\text{ cm}\) vs \(48.3\text{ cm}\)), they cannot have identical rankings in both categories.

Teaching callout: Notice how we didn't need to sort all 12 months in both categories. By focusing on tied values first, we found a contradiction immediately. This counterexample approach saves significant time compared to creating complete rankings for all months.

Statement 1 is NO.

ANALYZING STATEMENT 2

Statement 2 Translation:
Original: "For each month with an extreme low temperature of 0 degrees Celsius, the normal daily low temperature is less than 8 degrees Celsius."
What we're looking for:

• Identify all months with extreme low temperature = \(0°\mathrm{C}\)
• Check if ALL these months have normal daily low \(< 8°\mathrm{C}\)

In other words: Every time a month's extreme low hits exactly \(0°\mathrm{C}\), is its normal daily low always below \(8°\mathrm{C}\)?

The most efficient approach is to first sort by extreme low temperature to quickly identify all months with exactly \(0°\mathrm{C}\).

When we sort by extreme low temperature, we find three months with exactly \(0°\mathrm{C}\):

• April: \(0°\mathrm{C}\) extreme low, \(6.1°\mathrm{C}\) normal daily low
• May: \(0°\mathrm{C}\) extreme low, \(11.7°\mathrm{C}\) normal daily low
• October: \(0°\mathrm{C}\) extreme low, \(10.6°\mathrm{C}\) normal daily low

Now we check if all these months have normal daily low \(< 8°\mathrm{C}\):

• April: \(6.1°\mathrm{C}\) ✓ (less than \(8°\mathrm{C}\))
• May: \(11.7°\mathrm{C}\) ✗ (greater than \(8°\mathrm{C}\))
• October: \(10.6°\mathrm{C}\) ✗ (greater than \(8°\mathrm{C}\))

Since May and October have normal daily lows above \(8°\mathrm{C}\), we've found counterexamples.

Teaching callout: Sorting by extreme low temperature grouped our \(0°\mathrm{C}\) months together, making them easy to identify in one visual scan. This is much faster than checking all 12 months individually for the extreme low value.

Statement 2 is NO.

ANALYZING STATEMENT 3

Statement 3 Translation:
Original: "March is not ranked highest or lowest in any of the six categories shown."
What we're looking for:

• Check all six categories (normal high, normal low, extreme high, extreme low, normal precipitation, maximum precipitation)
• Verify March is neither at the top nor bottom of any ranking

In other words: March is always somewhere in the middle for all six measurements, never the extreme.

For this statement, we need to sort each column and check if March ever appears at position 1 (highest) or position 12 (lowest).

Let's check each category systematically:

1. Normal High Temperature: [Sort column] → March is not highest or lowest ✓
2. Normal Low Temperature: [Sort column] → March is not highest or lowest ✓
3. Extreme High Temperature: [Sort column] → March is not highest or lowest ✓
4. Extreme Low Temperature: [Sort column] → March is not highest or lowest ✓
5. Normal Precipitation: [Sort column] → March is not highest or lowest ✓
6. Maximum Precipitation: [Sort column] → March is not highest or lowest ✓

After checking all six categories, we confirm that March never appears at the extreme positions in any ranking.

Teaching callout: We didn't need to identify which specific months were at the extremes - we only needed to verify March wasn't there. This focused approach saved time compared to creating complete rankings for all categories.

Statement 3 is YES.

FINAL ANSWER COMPILATION

Looking at our analysis:

• Statement 1: NO (January and December tie in normal precipitation but have different maximum precipitation)
• Statement 2: NO (May and October have extreme low of \(0°\mathrm{C}\) but normal daily lows above \(8°\mathrm{C}\))
• Statement 3: YES (March is never ranked highest or lowest in any category)

The answer is: B (Statement 3 ONLY is YES)

LEARNING SUMMARY

Skills We Used

• Counterexample Detection: We quickly found contradictions rather than verifying all cases
• Strategic Sorting: We sorted data to group similar values and reveal patterns
• Pattern Recognition: We looked for ties and boundary values (like \(0°\mathrm{C}\)) that often reveal contradictions

Strategic Insights

• When a statement claims "all" or "always," finding just one counterexample is enough to disprove it
• Tied values are excellent places to look for counterexamples in ranking problems
• Sorting data makes visual scanning much more efficient than checking each value individually
• Focus on boundary conditions (like exactly \(0°\mathrm{C}\)) as they often contain valuable insights

Common Mistakes We Avoided

• We didn't waste time creating complete rankings for all 12 months in every category
• We stopped checking as soon as we found a definitive counterexample
• We focused only on the specific values needed to evaluate each statement
• We didn't calculate exact rankings when only checking for extremes

Remember that in GMAT table analysis questions, efficiency comes from knowing what NOT to calculate just as much as knowing what to calculate. By using sorting and strategic checking, we can solve these problems in a fraction of the time it would take to check everything manually.