The table displays information about certain pathogens that could pose a health risk if present in drinking water. Health significance:...

OWNING THE DATASET

Let's start by understanding what this table shows us about waterborne pathogens. We have a dataset that categorizes different disease-causing microorganisms by:

• Type: Whether they're bacteria, viruses, or protozoa
• Resistance to chlorine: Low (\(\leq 30\) min), Moderate (30-60 min), or High (\(> 60\) min)
• Persistence in water supplies: Short, Moderate, or Long
• Health significance: Low, Moderate, or High

Looking at one example row helps us understand the relationships:

• E. coli (pathogenic) | Bacteria | Low resistance | Moderate persistence | High health significance

Key insights:

• The table combines biological classification with public health characteristics
• Chlorine resistance tells us how quickly pathogens can be eliminated with treatment
• Some categories may have very few representatives (potential outliers to look for)

Now let's analyze each statement strategically, starting with the one that requires the least work.

ANALYZING STATEMENT 3 (Starting here because it's fastest)

Statement 3 Translation:
Original: "The only pathogen with low health significance has high resistance to chlorine."
What we're looking for:

• Find all pathogens with low health significance
• Check if there's only one, and if it has high chlorine resistance

In other words: Is the single pathogen classified as "low" health significance also highly resistant to chlorine?

Let's sort by "Health significance" in ascending order to immediately bring any "low" significance pathogens to the top.

SORT BY "Health significance" (ascending)

After sorting, we can immediately see there's only one pathogen with "low" health significance: Non-tuberculous mycobacteria. Now we just need to check its chlorine resistance level.

Looking at this pathogen's chlorine resistance value, we see it's "High" - meaning it takes more than 60 minutes to treat.

Therefore, Statement 3 is TRUE (YES).

Teaching note: Notice how sorting instantly brought our answer to the top of the table. Instead of scanning all rows looking for "low" health significance, the sort function did that work for us.

ANALYZING STATEMENT 2

Statement 2 Translation:
Original: "Only viruses have long persistence in water supplies."
What we're looking for:

• Identify pathogens with "Long" persistence
• Check if ALL of them are viruses (and none are bacteria or protozoa)

In other words: Are viruses the only type of pathogen that can persist in water for a long time?

For this statement, we need to find just one non-virus with long persistence to disprove it.

SORT BY "Persistence in water supplies" (descending)

After sorting, all pathogens with "Long" persistence are grouped together at the top. Now we can quickly scan the "Type" column for these long-persistence pathogens.

Looking at the sorted data, we can see both bacteria and protozoa with "Long" persistence. For example, we can immediately spot:

• Bacteria with long persistence (such as Legionella)
• Protozoa with long persistence (such as Cryptosporidium)

Since we found non-virus pathogens with long persistence, Statement 2 is FALSE (NO).

Teaching note: We didn't need to count all instances or create distributions. Once we found just one counterexample (a non-virus with long persistence), we could immediately conclude the statement was false. This is called "disproving by counterexample" - a powerful time-saver!

ANALYZING STATEMENT 1

Statement 1 Translation:
Original: "A majority of bacteria can be treated with chlorine in 30 minutes or less."
What we're looking for:

• Count how many bacteria are in the table
• Determine how many have "Low" resistance to chlorine (\(\leq 30\) min)
• Check if this represents more than \(50\%\) (a majority)

In other words: Do more than half of all bacteria have low chlorine resistance?

Let's sort the data to make our analysis efficient:

SORT BY "Type" then "Resistance to chlorine"

With this sorting, all bacteria are grouped together, and within that group, they're organized by resistance level. This makes counting much easier.

Now we can see:

1. Total bacteria in the table: 12
2. Bacteria with "Low" resistance (\(\leq 30\) min): 10
3. Bacteria with "Moderate" resistance (30-60 min): 1
4. Bacteria with "High" resistance (\(> 60\) min): 1

For this statement, we need to determine if a majority (more than \(50\%\)) of bacteria can be treated in 30 minutes or less, which means having "Low" resistance.

10 out of 12 bacteria have "Low" resistance, which is \(83.3\%\) - well above the \(50\%\) threshold needed for a majority.

Therefore, Statement 1 is TRUE (YES).

Teaching note: We didn't need to calculate the exact percentage (\(83.3\%\)). For a "majority" claim, we just needed to confirm it was greater than \(50\%\). Once we saw 10 out of 12 (which is clearly more than 6 out of 12), we knew the statement was true.

FINAL ANSWER COMPILATION

Based on our analysis:

• Statement 1: TRUE (YES) - A majority of bacteria can be treated with chlorine in 30 minutes or less
• Statement 2: FALSE (NO) - Not only viruses have long persistence in water supplies
• Statement 3: TRUE (YES) - The only pathogen with low health significance does have high resistance to chlorine

Our answer is: YES, NO, YES

LEARNING SUMMARY

Skills We Used

• Strategic Sorting: We let the computer do the hard work by sorting data in ways that made patterns immediately visible
• Visual Pattern Recognition: After sorting, we could quickly see distributions without manual counting
• Efficient Validation: For Statement 2, we used counterexample disproving instead of comprehensive counting

Strategic Insights

1. Tackle statements in order of efficiency: We started with Statement 3 because it required checking just one data point
2. Sort to reveal patterns: Different sorting strategies (by health significance, persistence, type) revealed different insights
3. Use early stopping conditions: For Statement 2, we stopped once we found one counterexample
4. Skip unnecessary precision: For Statement 1, we just needed to confirm \(> 50\%\), not calculate \(83.3\%\)

Common Mistakes We Avoided

• Manually scanning the entire table multiple times for different criteria
• Calculating exact percentages when only the majority/minority distinction mattered
• Checking all instances when one counterexample was sufficient
• Looking at data randomly instead of using sorting to group critical information

Remember: In GMAT Table Analysis questions, appropriate sorting transforms calculation-heavy problems into simple visual pattern recognition tasks. Always ask: "How can I reorganize this data to make the answer obvious?"