Immigrant Statistics for 20 Countries Number of people in country who are refugees and/or asylum seekers from other countries Country...

OWNING THE DATASET

Let's start by understanding what we're working with, focusing on the key elements that will help us solve efficiently:

Our table contains refugee/asylum seeker counts for 20 countries (labeled A through T) across three years (1995, 2000, and 2005). For example, a typical row might look like:

Country	1995	2000	2005
Country F	215,743	230,892	241,365

Key insight: This dataset contains ONLY numerical refugee counts - there's no information about geographic location, political stability, or relationships between countries. This limitation will be crucial when evaluating certain statements.

Note: When approaching any table analysis question, always scan the column headers first to understand what information is actually available. This prevents us from wasting time searching for data that doesn't exist.

ANALYZING STATEMENT 3 (Starting here for maximum efficiency)

Statement 3 Translation:

Original: "Politically stable countries tend to have more refugees and asylum seekers from neighboring countries than do politically unstable countries."

What we're looking for:

• A comparison between politically stable vs. unstable countries
• Information about the origin of refugees (neighboring vs. non-neighboring)

In other words: Do stable countries have more refugees from neighbors than unstable countries do?

Let's evaluate this claim by checking our data. Wait - scanning our table headers again, we can see we only have:

• Country identifiers (A-T)
• Refugee/asylum seeker counts for three years

Crucial observation: Our dataset contains no information about:

• Which countries are politically stable or unstable
• Which countries are neighbors
• Where the refugees originated from

Since we have none of this information, we cannot possibly evaluate whether Statement 3 is true or false. The statement requires data we simply don't have.

Statement 3 is No.

Teaching note: This is why "owning the dataset" is so important. By understanding exactly what information is available, we can immediately recognize when a statement requires data we don't have. No calculations needed - this approach saves significant effort!

ANALYZING STATEMENT 1

Statement 1 Translation:

Original: "Country T had the minimum number of refugees and asylum seekers in each of the three years shown."

What we're looking for:

• Country T needs to have the lowest count in 1995
• Country T needs to have the lowest count in 2000
• Country T needs to have the lowest count in 2005

In other words: Was Country T at the bottom of the list in all three years?

To check this efficiently, let's sort the data by the 1995 column in ascending order. This immediately shows us which country had the minimum number in 1995.

After sorting by 1995 (ascending), we see that Country M is at the top of our sorted list - not Country T. This means Country M had the minimum number of refugees/asylum seekers in 1995.

Since Statement 1 claims Country T had the minimum in ALL THREE years, finding just one year where this isn't true is enough to disprove the entire statement. We've found our counterexample!

Statement 1 is No.

Teaching note: Notice how we didn't need to check 2000 or 2005 at all. When a statement requires something to be true across multiple cases, finding a single counterexample is enough to disprove it. This is much faster than checking every year!

ANALYZING STATEMENT 2

Statement 2 Translation:

Original: "The median number of refugees and asylum seekers in 2005 was 245,986."

What we're looking for:

• The middle value in the 2005 data (when arranged in order)
• Whether this middle value equals 245,986

In other words: When we arrange all 20 countries by their 2005 refugee counts, is the median 245,986?

Let's sort our data by the 2005 column in ascending order. Since we have 20 countries, the median will be the average of the 10th and 11th values.

After sorting, we find:

• 10th value: 240,341
• 11th value: 240,701

These are the two middle values in our dataset. The median would be their average: \((240,341 + 240,701) ÷ 2\).

But wait - we don't even need to calculate this precisely! Both values are less than 245,986, so their average must also be less than 245,986. The median cannot possibly be 245,986.

Statement 2 is No.

Teaching note: When comparing values, we often don't need exact calculations. Here, we recognized that both middle values were below the claimed median, so no calculation was necessary. This approach saves time and reduces the chance of arithmetic errors.

FINAL ANSWER COMPILATION

After evaluating all three statements:

• Statement 1: No (Country T did not have the minimum in 1995)
• Statement 2: No (The median in 2005 was not 245,986)
• Statement 3: No (Insufficient data to evaluate the claim)

Since all three statements are no, the correct answer is: None of the statements is yes.

LEARNING SUMMARY

Skills We Used

• Data limitation recognition: Immediately identifying when a statement requires information not present in our dataset (Statement 3)
• Counterexample efficiency: Disproving an "all cases" claim by finding just one exception (Statement 1)
• Comparison without calculation: Determining if a specific value could be the median without calculating the exact median (Statement 2)

Strategic Insights

1. Order matters: We tackled the statements in order of solution efficiency (3→1→2), not in numerical order
2. Sorting is powerful: Sorting transformed complex comparison tasks into simple visual scans
3. Minimum information principle: We gathered only the specific information needed to evaluate each claim, avoiding unnecessary work

Common Mistakes We Avoided

• Wasting time looking for non-existent information
• Checking all years when one counterexample was sufficient
• Calculating exact values when only directional comparison was needed

Remember: In table analysis questions, your first task is always to understand what information the table actually contains. This awareness prevents wasting time on statements that can be quickly evaluated or eliminated based on data limitations.