Population vs sample: what’s the difference?

Population vs sample
A population is the stallion group that you want to draw conclusions about .
A sample is the specific group that you will collect data from. The size of the sample is constantly less than the entire size of the population .
In research, a population doesn ’ thyroxine constantly refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organizations, countries, species, organisms, etc.

Population vs sample

Population
Sample

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The top 50 search results for advertisements for IT jobs in the Netherlands on May 1, 2020

Songs from the Eurovision Song Contest
Winning songs from the Eurovision Song Contest that were performed in English

Undergraduate students in the Netherlands
300 undergraduate students from three Dutch universities who volunteer for your psychology research study

All countries of the world
Countries with published data available on birth rates and GDP since 2000

Collecting data from a population

Populations are used when your research question requires, or when you have access to, data from every member of the population .
normally, it is alone square to collect data from a unharmed population when it is minor, accessible and cooperative .
Example: Collecting data from a populationA high school administrator wants to analyze the final exam scores of all graduating seniors to see if there is a trend. Since they are only interested in applying their findings to the graduating seniors in this high school, they use the whole population dataset.
For larger and more disperse populations, it is much unmanageable or impossible to collect data from every individual. For exercise, every 10 years, the federal US government aims to count every person living in the country using the US Census. This datum is used to distribute fund across the state .
however, historically, marginalized and low-income groups have been unmanageable to contact, locate and encourage participation from. Because of non-responses, the population count is incomplete and biased towards some groups, which results in disproportionate fund across the country .
In cases like this, sampling can be used to make more accurate inferences about the population .

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Collecting data from a sample

When your population is big in size, geographically dispersed, or difficult to contact, it ’ mho necessary to use a sample distribution. With statistical analysis, you can use sample data to make estimates or test hypotheses about population data .
Example: Collecting data from a sampleYou want to study political attitudes in young people. Your population is the 300,000 undergraduate students in the Netherlands. Because it’s not practical to collect data from all of them, you use a sample of 300 undergraduate volunteers from three Dutch universities – this is the group who will complete your online survey.
ideally, a sample should be randomly selected and spokesperson of the population. Using probability sampling methods ( such as dim-witted random sampling or stratified sampling ) reduces the gamble of sampling bias and enhances both internal and external validity.

For practical reasons, researchers frequently use non-probability sampling methods. Non-probability samples are chosen for specific criteria ; they may be more convenient or cheaper to access. Because of non-random choice methods, any statistical inferences about the broader population will be weaker than with a probability sample distribution .

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Reasons for sampling

  • Necessity: Sometimes it’s simply not possible to study the whole population due to its size or inaccessibility.
  • Practicality: It’s easier and more efficient to collect data from a sample.
  • Cost-effectiveness: There are fewer participant, laboratory, equipment, and researcher costs involved.
  • Manageability: Storing and running statistical analyses on smaller datasets is easier and reliable.

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Population parameter vs sample statistic

When you collect data from a population or a sample, there are assorted measurements and numbers you can calculate from the data. A parameter is a measure that describes the solid population. A statistic is a measure that describes the sample .
You can use estimate or guess testing to estimate how likely it is that a sample statistic differs from the population parameter .
Research example: Parameters and statisticsIn your study of students’ political attitudes, you ask your survey participants to rate themselves on a scale from 1, very liberal, to 7, very conservative. You find that most of your sample identifies as liberal – the mean rating on the political attitudes scale is 3.2.
You can use this statistic, the sample mean of 3.2, to make a scientific guess about the population parameter – that is, to infer the intend political attitude rat of all undergraduate students in the Netherlands .

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Sampling error

A sampling error is the difference between a population argument and a sample statistic. In your analyze, the sampling erroneousness is the dispute between the mean political attitude military rank of your sample distribution and the true entail political position evaluation of all undergraduate students in the Netherlands .
Sampling errors happen even when you use a randomly selected sample distribution. This is because random samples are not identical to the population in terms of numeric measures like means and standard deviations.

Because the aim of scientific research is to generalize findings from the sample distribution to the population, you want the sampling error to be depleted. You can reduce sampling error by increasing the sample size .

Quiz: Populations vs samples

Frequently asked questions about samples and populations