Numerical variables are a fundamental concept in statistics and data science. They can take on any value within a finite or infinite interval, making them essential for quantitative analysis. Numerical variables are divided into two main categories: continuous and discrete.

Continuous variables can assume an infinite number of values within a given range. Examples include height, weight, and temperature, which can be expressed as decimal numbers (e.g., 5.6, 7.8). On the other hand, discrete variables can only take on specific, countable values, such as the number of children or the number of cars in a parking lot. These values are often whole numbers, but they can also include fractions in certain contexts, like average counts ¹⁴.

Understanding numerical variables is crucial for performing statistical analyses, as they allow for the calculation of measures of central tendency, such as mean, median, and mode, which summarize data sets effectively ¹.