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An observation in statistics refers to the process of collecting and recording data or information about a particular phenomenon or group of individuals. It involves systematically observing and recording various variables, such as numerical values or categorical characteristics, in order to gain a better understanding of the subject being studied. Observations play a crucial role in statistical analysis as they provide the foundation for making inferences and drawing conclusions about a larger population. They can be obtained through various methods, such as surveys, experiments, or direct measurements, and are used to generate valuable insights and insights into patterns and trends within data. Overall, observations are a fundamental aspect of statistics that allow researchers to gather and analyze data in a structured and objective manner.
What is an Observation in Statistics?
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?