Explanation:

Introduction to Mutually Exclusive Classification:
Mutually exclusive classification refers to a situation where each observation or data point can only belong to one category or class. In other words, the categories or classes are distinct and non-overlapping. This concept is commonly used in statistical analysis and machine learning algorithms for classifying data into different groups based on certain characteristics or attributes.

Difference between Discrete and Continuous Variables:
- Discrete Variables: Discrete variables can only take on specific values and have clear boundaries. These values are often integers or whole numbers. For example, the number of children in a family, the number of cars in a parking lot, or the number of items in a shopping cart are all discrete variables.

- Continuous Variables: Continuous variables, on the other hand, can take on any value within a certain range or interval. They are not limited to specific values or whole numbers. For example, height, weight, temperature, or time are all examples of continuous variables.

Mutually Exclusive Classification and Continuous Variables:
The correct answer to the question is option 'B' - a continuous variable. Mutually exclusive classification is usually meant for continuous variables. This is because continuous variables can have an infinite number of possible values within a given range, and it is important to assign each observation to a specific category or class.

For example, let's consider a dataset of students' heights. We want to classify each student as either "short," "average," or "tall." Since height is a continuous variable, each student can have a different height within a specific range. In this case, we need to use mutually exclusive classification to assign each student to one of the three categories based on their height.

On the other hand, discrete variables already have clear boundaries or distinct values, making them naturally mutually exclusive. For example, if we have a dataset of colors (red, blue, green), each color is already mutually exclusive as there is no overlap or ambiguity in assigning each observation to a specific color category.

Conclusion:
In summary, mutually exclusive classification is usually meant for continuous variables. Continuous variables can have an infinite number of possible values within a given range, requiring the use of mutually exclusive classification to assign each observation to a specific category or class. Discrete variables, on the other hand, are already naturally mutually exclusive due to their distinct values or clear boundaries.