When working with data sets and conducting a statistical analysis, you need to ensure that the data set you are using is relevant, valid, and correct.
The appropriate data will help you make sure that you have a correct outcome and come to an effective conclusion and solution that solves the problem at hand.
This is why it's necessary to know the difference between population data sets and sample data sets and whether the data you are dealing with is part of a population data set or a sample data set.
In this brief guide, you will learn the differences between these two popular statistical terms.
Let's get started!
What Is A Population in Statistics? Population Definition
A population is a collection that consists of all possible data values and items within the field of study.
A population refers to the whole number of items or the entire group of people that are of interest in the statistical study.
Essentially, it makes up the entire pool of the study.
An example of a population set is the number of all the people living in a country, such as all the number of people living in the U.S. – that is, the entire population of the U.S. .
Another example of working with a population set could be analyzing all the students in a university – this is the whole number of students studying at the University.
The quantity that describes the outcome of measuring the whole population is called a parameter. A parameter is a number that refers to the entire population.
Which Method Should You Use to Collect Data from a Population?
You may want to choose to collect data from a population when you need to work with a large amount of data.
A way of collecting data from an entire population is by conducting a census.
Let's take the U.S. census as an example. It's a procedure that takes place at least once every ten years.
It counts every person living in the U.S. and conducts a survey that collects data from all individuals and every member that makes up the population.
Is Population Data Accurate?
Collecting data from a population is not the most efficient way of collecting data.
Populations are often hard to define and observe, which will inevitably introduce a bias in the study and probably skew the results and lead to unreliable conclusions.
There are a few reasons why this is the case:
- The pool of study is often too large.
- There may be geographical constraints.
- There may be time constraints.
- There may be resource constraints
- There may be accessibility constraints.
- It is likely that there will be missing data values.
Instead, you may choose to collect data from a population when the population size is relatively small. You can also gather information on the items/people that make up the population when it is easily accessible, or when you can measure the items or contact every member of the population.
What Is a Sample in Statistics? Sample Definition
A sample is a subset and a small portion of the population – a small part of all the possible data values that are part of the specified field of study.
The size of the sample data set will always be smaller than that of the population.
Working with sample data is helpful when the population is too large and not reliable.
For example, the population could be unknown in size, or even not measurable or infinite in size.
This is the preferred method of collecting data when the data you need is too hard to gather. It's a way to get information about the population without actually needing to access every person or item in that population.
The number that refers to the result of measuring from within a sample data set is called a statistic. A statistic describes a sample of a population.
What Are the Defining Characteristics of a Good Sample?
A sample should accurately represent the whole population.
One of the other most important characteristics of sample data is that it should be random and chosen without bias.
Insights and data should be collected randomly, meaning every item or member of a population has equal chances and the same probability of being selected.
Those two criteria reduce bias and ensure the results are valid.
How Is Data Collected from a Sample?
The process of collecting data from a small subset of the population is known as sampling.
Sampling is helpful when it is difficult to collect all the necessary data from the population.
Sampling represents the entire population as it generalizes and reflects the individuals that are part of it.
Gathering all the necessary information and contacting the members of interest is easier, less time-consuming, and less costly.
A way to collect data from a sample is to conduct a poll, which is what happens during an election period.
Polls are a helpful tool for gauging voters' preferences and support of the parties taking part in the election.
It's impossible to gather all registered voters in the country and ask who they prefer to win the election since they might be in the millions.
Instead, it is better to gather several thousand responses from different sections of the population, such as from various cities and regions and from unrelated spots within those cities and regions.
This selection needs to be random, and people need to be chosen by chance. This ideally means that everyone should equally have the same chance of being picked for the poll.
What Is Sampling Bias and How to Avoid It
As mentioned earlier, a sample should accurately represent and reflect the entire population from which it has been taken.
For the sample to be representative, it should be gathered randomly. If not, the result of the analysis will most likely be prone to bias or what is otherwise known as sampling bias.
Sampling bias occurs when the methods used to collect the sample encourage systemic prejudice.
The methods are either in favor of or against an individual or group, which will inevitably skew the outcome of the analysis. Members of the specific population are not selected correclty, meaning they either have a higher or lower chance of being selected.
Essentially, the sample is collected in a way that unfairly favors only certain members of the population over others.
For example, a survey that questions students at the University’s cafe regarding their University experience excludes various groups of students.
- Students who are distance learning and studying from home.
- Students who may be studying part-time and working at the time the survey took place.
- Students on an exchange program in a different country.
- Students in a class following a lecture.
Firstly, this method is not random. Secondly, it is prone to sampling bias as it is limiting and favors only the section of students that were able to be present in the cafe during morning hours and therefore is not representative.
These students may have specific characteristics and probably do not reflect the overall population of students in the whole University.
Let's take another example.
Say that a poll is conducted during an election period to find out which candidate is the most favorable to the public.
If the members polled are only white collar workers, the results will be inaccurate since it doesn't accurately describe the entire population.
The population also includes blue-collar workers and people who might work more than one minimum wage job to make ends meet. The preferences for the candidate will likely differ from group to group.
In this case, the bias is heavy since the poll is not diverse – it reflects only one section of the population.
A way to lower the risk of sampling bias is through stratified random sampling.
Stratified random sampling involves accurately defining the population of interest, the characteristics it needs to have, and how you want it divided.
It also involves choosing your sample size and then dividing the sample into precise, homogenous smaller sub-groups that match the relevant criteria you set while ensuring the population and sample match.
Stratified random sampling leads to a more representative sample.
And there you have it! You now have a high-level understanding of the differences between two widely used statistical terms - population and sample.
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Thank you for reading!