Population or Sample: Main Differences and Definitions
A population is the complete group of subjects from which statistical data is derived. It could be a gathering of people, a collection of objects, or something else. It constitutes the study's data source.
A sample stands for the subgroup of the population that you will be using to represent the information. While additional data is beneficial for obtaining objective results, it is key to ensure that the data gathered is appropriate for the issue at hand.
You can achieve this by comparing population and sample sizes. You will learn everything you want to know about population vs. sample in this article.
Population vs. sample examples| Population | Sample |
|---|---|
| All residents of a country | Sample: All residents of a country who earn more than the poverty level |
| An office's entire workforce | All supervisors in the office, out of all the workers |
| Population: All Mediterranean dish recipes | Sample: All plant-based Mediterranean dish recipes |
Collecting population data
When your study issue necessitates a large amount of data about every participant, you gather data from a population. When the data source is modest and cooperative, you can use population data to get all the information you need. For bigger populations, sampling is used to represent areas of the population where data collection is difficult.
A researcher wants to look at the degree of professionalism among Florida nurses to see whether there is a pattern. They use the entire population dataset because they are only interested in applying their findings to licensed practical nurses in Florida.
Politicians and governments need population data filtered by age, sex, place of residence, and relationship status, as well as income, ethnicity, academic background, disability, and other important characteristics, to make confident decisions. After the data has been collected, it must be processed and analyzed, with forecasts of future demographic trends and recommendations for the interested parties included.
Collecting sample data
When the population is big and dispersed, or when collecting data on an individual level is difficult, researchers use samples. Then you can make basic assumptions based on a tiny sample of the population. Different research approaches, such as probability sampling and non-probability sampling, are used to generate data samples.
Samples must be chosen at random and must represent the whole population, including all classes. To ensure this, statistical analysis techniques and sampling methods are utilized to take random samples from each demographic segment. This reduces sample bias while also increasing validity.
It allows data researchers, analytics professionals, and other data scientists to deal with a limited, reasonable amount of data about a given population to swiftly create and run analytical models while still generating reliable results.
Consider the case of firm X, a laptop manufacturer that is developing a new range of products. Data must be gathered from relevant sources to do research on characteristics, purchase price, target audience, industry trends, and so on. Different data collection techniques, such as customer polls or focus groups, are available to the marketing team. Because polling millions of people about their preferences is impossible, they use a sample and gather the opinions of a few hundred people.
There are multiple techniques for selecting samples from data. Based on the sort of research and the quality of information needed, sampling methods differ by research type. Based on the sort of research and the quality of information needed, sampling methods differ by research type. Sampling can be based on probability, which employs random numbers to correspond to points in the data set to verify that the points chosen for the sample have no correlation. Other probability sampling variations include:
- Simple random sampling: Subjects are chosen randomly from the entire population using the software.
- Stratified sampling: Data sets or populations are divided into subsets based on a similar factor, and samples are taken at random from each segment.
- Cluster sampling: It divides the larger data set into subgroups based on a given factor and then analyzes a random sample of subgroups.
- Multistage sampling: It is a more involved version of cluster sampling that divides the broader population into multiple subgroups. After that, subgroups are separated based on a secondary factor, and they are sampled and evaluated. As many subgroups are located, grouped, and evaluated, this staging may continue.
Non-probability sampling is described as a data-collecting approach in which samples are chosen based on the scientist's personal judgment rather than random selection. It is a less strict approach. The scientists' knowledge is mainly reliant on this sampling strategy.
Sample statistic vs. population parameter
Researchers typically want to know something about populations but lack data for each member of the population. It would not be reasonable for a company's customer service team to contact each person who placed an order to find out if they were satisfied. Instead, the organization might choose a sample of that population. A sample is a subset of a population chosen to reflect the entire population.
The sample must be randomized to use statistics and learn about the population. A random sample is one in which each individual in a population has an equal probability of being chosen. A basic random sample is the most widely used sample. Every available sample of the chosen size must have a fair probability of being selected.
Using small samples, inferential statistics allow you to draw generalizations about populations. As a result, inferential statistics are extremely useful because it is rare to be able to sample an entire population.
Suppose you wish to determine the average income of Netflix subscribers—a population parameter. You select 100 members at random and calculate that their average annual salary is $30,000. (a statistic). You find that the average household income is going to be around $30,000.
A parameter is a set measure that describes the entire population, whereas a statistic is a feature of a sample, which is a subset of the population. A statistic is a known number and a variable that changes on the population percentage, whereas the parameter is a set, unknown value.
A fixed measure received the support of 30% of UK MPs. Because there are only 195 MPs, you can keep track of how each one voted.
Example: StatisticThe latest aid package has received 90% approval in the UK. Because researchers cannot ask millions of residents if they agree, they gather samples of a small portion of the population and compute the rest.