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Statistical population

A statistical population is a set of entities from which statistical inferences are to be drawn, or a set of similar items or events which is of interest for some question or experiment [1, 2]. Since in this case and many others it is impossible to observe the entire statistical population, due to time constraints, constraints of geographical accessibility, and constraints on the researcher’s resources, a researcher would instead observe a statistical sample from the population in order to attempt to learn something about the population as a whole.

A subset of a population that shares one or more additional properties is called a sub population [1, 2].

A sample is a set of individuals or objects collected or selected from a statistical population by a defined procedure [1, 3] The elements of a sample are known as sample points, sampling units or observations. Typically, the population is very large, making a census or a complete enumeration of all the values in the population impractical or impossible. The sample represents a subset of manageable size. Samples are collected and statistics are calculated from the samples so that one can make inferences or extrapolations from the sample to the population. This process of collecting information from a sample is referred to as sampling.

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Population in descriptive statistics

The population in descriptive statistics is a statistical population.

Descriptive statistics summarizes and describes simple, but key characteristics or variables observed in a population. It is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population. It provides simple summaries about the sample and about the observations that have been made [4, 6, 7].

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Population in inferential statistics

The population in inferential statistics is a sample.

Inferential statistics is based on a sample of an observed population from which inferences can be made about that population. It is concerned with generalizing from a sample to a population. It attempts to reach the conclusions to learn about the population; that extends beyond the data available [5, 6, 7].

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Differences

1. Descriptive statistics is concerned with describing the population under study. Sampling is not required. Inferential statistics focuses on drawing conclusions about the populations, based on sample analysis [6, 7].

2. Descriptive statistics does not state anything beyond the observed data points of a specific characteristic in population. While inferential statistics takes an extra step beyond a descriptive statistic and reasons an assumption based on the data and compared to other data [6, 7].

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References

1. https://courses.lumenlearning.com/boundless-statistics/chapter/populations-and-samples/

2. https://en.wikipedia.org/wiki/Statistical_population

3. https://en.wikipedia.org/wiki/Sample_(statistics)

4. https://en.wikipedia.org/wiki/Descriptive_statistics

5. https://en.wikipedia.org/wiki/Statistical_inference

6. https://in.springboard.com/blog/descriptive-vs-inferential-statistics/

7. https://www.methodspace.com/understanding-inferential-statistics-and-descriptive-statistics/