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When you collect data about an entire population, it's known as a **census**. A census includes every individual you are interested in studying. This means that during a census, all possible data points are collected, giving you a full and comprehensive view of the matter at hand. For instance, if you wanted to know something about all bicycle fatalities in the U.S. in 2008, and you have every single case recorded, that's a census.

On the other hand, a **sample** is a smaller group, or subset, drawn from a larger population. A sample is used when collecting data from the entire population is impractical or impossible. It should be representative of the whole population to provide accurate insights. For example, if you are studying bicycle fatalities across multiple years and only choose cases from 2008 and 2009, you are drawing a sample, not conducting a census.

Understanding whether you're looking at a census or a sample is fundamental to analyzing data correctly, as it determines the reliability and scope of your conclusions.

A **population characteristic** is a measure that describes a feature or trait of the entire population. It's a true value that pertains to every member of the target group. Because a population characteristic reflects the whole group, it's considered more reliable and accurate than a measure taken from a sample.

For instance, if you know the exact average age of all cyclists killed in the U.S. in 2008 is 41 years, this number is a population characteristic. It doesn't rely on estimations or predictions because it comes from the exhaustive data collected in a census.

• A population characteristic provides a full picture without needing inferential statistics.
• It eliminates sampling error, which can occur when only a portion of the population is studied.

Understanding population characteristics is crucial in studies that can access data for every individual relevant to the study, which enhances the overall accuracy of analyses.

The terms **statistic** and **population characteristic** are often used to distinguish between measures derived from samples and those from entire populations. A **statistic** is a numerical value that is calculated from a sample of the population. Statisticians use statistics to infer or estimate the population characteristics due to practical constraints like time and resources.

Meanwhile, a **population characteristic** is calculated from data concerning every individual in the population. Therefore, it is essentially error-free when capturing the whole data set, unlike a statistic which might be prone to errors because it is based on a subset of the population.

• Statistics require inferential methods to predict or approximate population characteristics.
• Population characteristics need no such prediction, as they constitute the actual values from the entire dataset.

When analyzing data, knowing whether you're working with a statistic or a population characteristic helps you understand the scope and limitations of your analysis. This distinction critically impacts how results are interpreted and used in decision-making processes.