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In statistics, numerical variables can be classified as either discrete or continuous:

**Discrete:** Variables that can only take on whole numbers. For example:

- Number of pets owned by a family (1, 2, 5, etc.)
- Number of people in a stadium (100, 500, 900, etc.)
- Number of cookies in a jar (3, 11, 22, etc.)

**Continuous:** Variables that can take on *any* number, including numbers with several values after the decimal point. For example:

- Height (70.3434277 inches)
- Weight (189.5 pounds)
- Time (14.226 seconds)

Rule of Thumb:

If you can

countthe items, then you are working with a discrete variable – e.g. counting the number of people in a stadium.

But if you can

measurethe items, you are working with a continuous variable – e.g. measuring height, weight, time, etc.

Using this rule of thumb, you can easily classify most variables as discrete or continuous.

However, one variable that can be tricky to classify is **age**. On one hand, you can *count* the age of a person in years (e.g. 40 years old) but you could also *measure* someone’s age to an exact number (e.g. 40.225 years old).

**So, is age a discrete or continuous variable?**

**Is Age Discrete or Continuous?**

**Technically speaking, age is a continuous variable because it can take on any value with any number of decimal places.**

If you know someone’s birth date, you can calculate their exact age including years, months, weeks, days, hours, seconds, etc. so it’s possible to say that someone is 6.225549 years old.

You couldn’t do the same thing with a discrete variable like “number of pets owned” by a family. For example, you couldn’t say that a family owns 6.225549 pets. They either own 6 or 7 pets.

**However, when conducting some type of statistical analysis, age is almost always treated as a discrete variable.**

Consider the following examples to illustrate this.

**Example 1: Using Age in Medical Studies**

Suppose a medical professional is conducting a study in which she wants to know how age, diet, and exercise affect blood pressure.

When collecting data on the individuals in the study, she will record their age using whole numbers like 27 years old, 30 years old, 45 years old, etc.

Although age is technically a continuous variable, she will treat it as a discrete variable and only collect data using whole numbers.

**Example 2: Using Age in Biological Studies**

Suppose a biologist wants to understand the correlation between plant height and plant age.

When calculating data on individual plants, she will measure their height in centimeters and measure their age in either days, weeks, or months. For example, she might measure their age as 22 days, 29 days, 34 days, etc.

Although she could measure age as 22.4543 days, 29.8868 days, 34.0001 days, etc. she will likely measure it using whole numbers as this is easier to do.

**Summary**

If you’re asked whether age is a continuous or discrete variable in an Introductory Statistics class, the correct answer is technically continuous.

However, in the real world age is often treated as a discrete variable because it makes more sense when collecting data and when reporting the results of a study.

**Additional Resources**

Why is Statistics Important? (10 Reasons Statistics Matters!)

Qualitative vs. Quantitative Variables: What’s the Difference?

Levels of Measurement: Nominal, Ordinal, Interval and Ratio