For each of the 7 zones of a national park, the table provides information about policies pertaining to camping and...

OWNING THE DATASET

Let's start by understanding what we're working with in this camping zones table:

Zone	Camping in designated sites	Camping outside designated sites	Campfire outside
1	No	Yes	Yes
2	Yes	No	No
3	Yes	Yes	Yes
4	Yes	No	Yes
5	No	Yes	No
6	No	Yes	No
7	Yes	Yes	Yes

Key insights about our dataset:

• We have 7 zones with binary (yes/no) values across 3 policy categories
• Each statement will involve counting zones that meet specific combinations of criteria
• With only 7 zones and yes/no values, this table is perfect for sorting and visual counting
• The zones don't follow any particular pattern in their current order, making sorting especially valuable

This type of dataset allows us to use visual pattern recognition rather than calculation-heavy approaches - a major efficiency advantage!

ANALYZING STATEMENT 1

Statement 1 Translation:
Original: "All zones allow camping in at least some areas"
What we're looking for:

• Every zone must allow camping in at least one of the two camping categories
• If any zone has "No" for both camping options, the statement is false

In other words: Does each zone allow camping somewhere (either in designated sites OR outside)?

Let's approach this efficiently by sorting the table to group similar zones together:

1. Sort by "Camping in designated sites" to group all "No" zones together:

Zone	Camping in designated sites	Camping outside designated sites	Campfire outside
1	No	Yes	Yes
5	No	Yes	No
6	No	Yes	No
2	Yes	No	No
3	Yes	Yes	Yes
4	Yes	No	Yes
7	Yes	Yes	Yes

1. Examine only the "No" zones (zones 1, 5, and 6) to see if any of them have "No" in both camping columns:

• Zone 1: Has "No" for designated sites but "Yes" for outside camping
• Zone 5: Has "No" for designated sites but "Yes" for outside camping
• Zone 6: Has "No" for designated sites but "Yes" for outside camping

1. Draw conclusion: All three zones that don't allow camping in designated sites DO allow camping outside. Therefore, every zone allows camping in at least one location.

Statement 1 is Yes.

Teaching note: Notice how sorting transformed this question from checking all 7 zones individually into a focused check of just 3 zones. This is much faster than evaluating each zone separately!

ANALYZING STATEMENT 2

Statement 2 Translation:
Original: "Exactly 3 zones that allow camping in designated campsites also allow camping outside"
What we're looking for:

• Count zones that have "Yes" for BOTH "Camping in designated sites" AND "Camping outside designated sites"
• Check if that count equals exactly 3

In other words: Are there exactly 3 zones where both types of camping are allowed?

Let's use sorting again to make this count easy:

1. Sort by "Camping in designated sites" to group all "Yes" zones together:

Zone	Camping in designated sites	Camping outside designated sites	Campfire outside
1	No	Yes	Yes
5	No	Yes	No
6	No	Yes	No
2	Yes	No	No
3	Yes	Yes	Yes
4	Yes	No	Yes
7	Yes	Yes	Yes

1. Focus only on the "Yes" zones for designated sites (zones 2, 3, 4, and 7)

• Of these, which also have "Yes" for camping outside?
• Zone 3: Yes
• Zone 7: Yes
• That's only 2 zones, not 3

1. Draw conclusion: Only 2 zones (not 3) allow both types of camping.

Statement 2 is No.

Teaching note: By sorting first, we narrowed our focus to just the relevant zones, making our count more reliable and efficient. This approach eliminates the need to check every zone multiple times.

ANALYZING STATEMENT 3

Statement 3 Translation:
Original: "Exactly 2 zones that allow camping outside do NOT allow campfires outside"
What we're looking for:

• Count zones that have "Yes" for "Camping outside designated sites" AND "No" for "Campfire outside"
• Check if that count equals exactly 2

In other words: Of the zones where outside camping is allowed, are there exactly 2 where campfires are prohibited?

This requires us to look at multiple criteria, so let's use a two-step sorting approach:

1. First, sort by "Camping outside designated sites" to group all zones that allow outside camping:

Zone	Camping in designated sites	Camping outside designated sites	Campfire outside
2	Yes	No	No
4	Yes	No	Yes
1	No	Yes	Yes
3	Yes	Yes	Yes
5	No	Yes	No
6	No	Yes	No
7	Yes	Yes	Yes

1. Now focus only on the "Yes" zones for outside camping (zones 1, 3, 5, 6, and 7)

• Of these, which have "No" for campfires outside?
• Zone 5: No campfires allowed
• Zone 6: No campfires allowed
• That's exactly 2 zones

1. Draw conclusion: Exactly 2 zones that allow camping outside don't allow campfires outside.

Statement 3 is Yes.

Teaching note: This multi-criteria question becomes much simpler when we use sorting to create logical groupings. Rather than checking all 7 zones against both criteria, we quickly narrowed to the relevant subset, then counted the matches.

FINAL ANSWER COMPILATION

Evaluating the three statements:

• Statement 1: Yes - All zones allow camping in at least some areas
• Statement 2: No - Only 2 zones (not 3) that allow camping in designated sites also allow camping outside
• Statement 3: Yes - Exactly 2 zones that allow camping outside do not allow campfires outside

Therefore, our answer is: Yes No Yes

LEARNING SUMMARY

Skills We Used

• Sorting for pattern recognition: Instead of checking each zone individually, we sorted the data to create visual patterns that made counting much easier
• Focused analysis: For each statement, we identified exactly which subset of zones was relevant, avoiding unnecessary checks
• Visual scanning: After sorting, we used quick visual scanning rather than tedious individual checks

Strategic Insights

• Binary data advantage: When working with yes/no data, sorting is almost always the fastest approach
• Sort by primary criterion first: Always sort first by the main characteristic mentioned in the statement
• Multiple sorts for complex criteria: For Statement 3, we needed to focus on zones meeting two specific criteria, so we started with the first criterion to narrow down our search

Common Mistakes We Avoided

• Checking every zone individually: This would have required at least 21 separate checks (7 zones × 3 statements)
• Recounting repeatedly: Without sorting, we might have counted the same categories multiple times
• Overlooking the power of yes/no patterns: In binary tables, patterns become instantly visible after sorting

Remember: When approaching any table with yes/no or categorical data, sorting is your most powerful first step. It transforms counting problems into visual pattern recognition, which is much faster and more reliable under test conditions!