자료 분류 방법: 수치 자료

Discrete numerical data consists of countable, finite values like the number of children in a family or fish in a pond. Continuous numerical data can take infinite values across a range, including decimals and fractions, such as milk production measured as 54.8 or 75.5 liters. Both are types of quantitative data measurable in specific units.

Discrete data includes countable items with whole number values. Examples are the number of phone calls received daily, students enrolled in a statistics course, or books carried in a backpack. These values cannot be fractional—you cannot have 2.5 phone calls or 3.7 students.

Continuous data result from measurements and can include fractions, decimals, or irrational numbers within a range. For example, phone call duration might be 2.4 or 7.5 minutes, or backpack weight could be any value between zero and maximum. Discrete data, by contrast, only take specific whole number values.

Numerical data, also called quantitative data, are countable or measurable values expressed in specific units. They always represent numbers resulting from counting or measuring population attributes. Examples include pulse rate, weight, amount of money, and number of people in a town.

Continuous data commonly result from measurements like length, weight, and time. Examples include the weight of backpacks carried by students, duration of phone calls measured in minutes with decimals, or milk production volumes like 99.56 liters. These measurements can take any value within a continuous range.

Numerical data is one major classification of data types. Unlike how data are classified categorical data, which describe qualities or categories, numerical data represent measurable quantities. Understanding this distinction helps statisticians choose appropriate analysis methods and measurement levels for their research.

Distinguishing between discrete and continuous data is crucial for statistical analysis and interpretation. Discrete data requires counting methods, while continuous data involves measurement and may include decimal values. This classification determines which statistical techniques and levels of measurement are appropriate for analysis.