In statistics, an **observation** is simply one occurrence of something you’re measuring.

For example, suppose you’re measuring the weight of a certain species of turtle. Each turtle that you collect the weight for counts as one single observation.

The following dataset contains the weight of 15 different turtles, so there are **15 **total observations:

When viewing a dataset in statistical software like Excel, R, Python, or Stata, the number of rows in the dataset is equal to the number of observations.

For example, a dataset with 100 rows has 100 observations.

It’s also interesting to note that a single observation can be associated with **multiple variables**. For example, in the following dataset there are 15 observations and 3 variables:

The first observation has the following values for the three variables:

- Weight: 290 pounds, Length: 30 inches, Region: East

The second observation has the following values for the three variables:

- Weight: 296 pounds, Length: 35 inches, Region: East

And so on.

It’s also worth noting that the total number of observations is equal to the **sample size** of the dataset. For example, a dataset that has 100 observations has a sample size of 100.

**Additional Resources**

Descriptive vs. Inferential Statistics: What’s the Difference?

Population vs. Sample: What’s the Difference?

Statistic vs. Parameter: What’s the Difference?