Statistical population
A statistical population is a collection of individuals or objects that possess some common characteristics of interest. It is the entire set from which a statistical sample is drawn. The population can be divided into two categories: the target population, which is the entire set of elements in the population that possess the characteristic of interest, and the sampling population, which consists of those elements from the target population that are actually chosen for the sample. *The size of the population is typically very large, so it is typically not feasible to collect data from every element in the population.* The population is often divided into sub-populations so that data can be collected more easily. *Data collected from a statistical population is used to make inferences about the population as a whole.*
Example of Statistical population
A statistical population can consist of any set of individuals or objects that share a common characteristic. For example, the population of students enrolled in a particular school or the population of cars produced by a particular manufacturer. *In the case of students, the population would contain all of the students enrolled in the school, and in the case of cars, the population would contain all of the cars produced by the manufacturer.* The population can also consist of things that cannot be counted, such as the population of opinions on a particular issue or the population of possible solutions to a problem. *In these cases, data can be collected from a sample of the population in order to make inferences about the population as a whole.*
When to use Statistical population
Statistical population is used when it is not feasible to collect data from every element in the population. It is used for collecting data from a subset of the population and making inferences about the population as a whole. *It is used to gain information about the entire population from a sample of the population.* Statistical population is also used in survey research, where researchers collect data from a sample of the population in order to determine the characteristics of the entire population. *It is also used in experiments, where an experiment is performed on a sample of the population in order to draw conclusions about the population as a whole.*
Types of Statistical population
There are three main types of statistical populations: finite, infinite, and hybrid. *A finite population is one that has a fixed number of elements and is completely known. This type of population is typically used in surveys or censuses where each element of the population is accounted for.* An infinite population is one that is potentially unbounded, such as the population of real numbers. *In a hybrid population, some elements of the population are known, while others are estimated.*
Steps of using statistical population
- Define the population: The first step in any statistical analysis is to define the population, which is the entire set of elements in the population that possess the characteristic of interest.
- Identify a sample: The second step is to identify a sample from the population, which consists of those elements from the target population that are actually chosen for the sample.
- Collect data: The third step is to collect data from the sample.
- Analyze data: The fourth step is to analyze the data in order to make inferences about the population as a whole.
Advantages of Statistical population
- Statistical population provides a complete picture of the characteristics of the population of interest, allowing for more accurate and precise estimates of the characteristics of the population.
- Statistical population data is also more reliable than a sample because all elements in the population are included in the data collection process.
- Population data is also more economical because it does not require the additional costs associated with sampling and data collection.
- Finally, population data allows for more precise estimation of population parameters such as mean, median, and standard deviation.
Limitations of Statistical population
- The size of the population is often very large, making it impossible to collect data from all elements in the population.
- This means that the sample data may not be representative of the population as a whole, which can lead to inaccurate results.
- Additionally, *it is possible that the characteristics of the population may change over time, which means that the sample data may not be representative of the population at the time of analysis.
- Lastly, *the sample data may contain errors due to sampling errors, human error or instrument error, which can also lead to inaccurate results.
There are several other approaches related to statistical populations that are used to collect data.
- Survey sampling is a method of taking a sample of a population using a variety of techniques such as random sampling, systematic sampling, and stratified sampling.
- Cluster sampling is a technique used to select a sample where the population is divided into groups or clusters and a sample of these groups is selected. *The census is a method of collecting data from the entire population, which is the most accurate way of collecting data.
- Quota sampling is a method of sampling that uses predetermined quotas to ensure that specific population characteristics are represented in the sample. Finally, captive sampling is a method of sampling where the sample is taken from a group of individuals who are already part of a larger population.
Statistical population — recommended articles |
Systematic sampling techniques — Quantitative research — Measurement method — Stratified random sampling — Sampling error — Quantitative variable — Method of moments — Measurement uncertainty — Analysis of variance |
References
- Balding, D. J. (2006). A tutorial on statistical methods for population association studies. Nature reviews genetics, 7(10), 781-791.