To determine if the data are discrete or continuous, we need to know if the data can take on any value within a range or if they are limited to specific values:

1. Discrete Data: Values are countable and typically whole numbers (e.g., number of people, shoe sizes). Discrete data cannot have fractional values within the range.

2. Continuous Data: Values can take on any real number within a range (e.g., height, weight, time). Continuous data include fractional or decimal values.

Based on these definitions:

• Option A is correct if the data can take on any real value (indicating continuous data).
• Option B is correct if the data can only have whole number values (indicating discrete data).

So, without knowing the actual data, let’s match the options with definitions:

• A. The data are continuous because they can take on any real value: This statement is correct if the data is continuous.
• B. The data are discrete because they can only have whole number values: This is correct if the data is discrete.

Explanation:

• Choose A if data can be any real value (continuous).
• Choose B if data is countable in whole numbers (discrete).

Would you like further clarification on discrete and continuous data types?

---

Here are five related questions to deepen understanding:

1. What are examples of discrete data in real life?
2. How can we identify continuous data in scientific research?
3. How does the measurement process affect whether data is discrete or continuous?
4. Can data be transformed from discrete to continuous? How?
5. Why is it essential to know if data is discrete or continuous in statistical analysis?

Tip: Continuous data often comes from measurements, while discrete data usually arises from counting.