What Is A Representative Sample?
A Representative sample is a sampling technique where the researcher selects a small group of individuals who demonstrate the characteristics of the entire Population. This method aims to provide a fair and accurate representation of the total population size without spending time, money, and other resources surveying the whole population.
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A representative sample in statistics is especially beneficial when the researcher needs to study a vastly diverse population. Selecting a small pool of people from a massive and widespread population is the most efficient and cost-effective way to attain the most direct and precise results.
Table of contents
- What Is A Representative Sample?
- Representative Sample Explained
- How To Get?
- Examples
- Importance
- Representative Sample Vs. Random Sample Vs. Biased Sample
- Representative Sample Vs. Non-Representative Sample
- Frequently Asked Questions (FAQs)
- Recommended Articles
Key Takeaways
- A representative sample is a sampling method in which the researcher selects a small pool of individuals with the same traits and attributes as the entire population.
- This survey method is advantageous for researchers studying a large, diverse, and widely-spread target audience for their project.
- Selecting a small group of people from the total population ensures that the researcher gets a fair and accurate representation of the entire population efficiently and cost-effectively instead of spending money and other resources surveying the total population.
Representative Sample Explained
In the representative sample method, the researcher selects a subset of the target audience to conduct a study. This technique allows the researcher to choose a small group of individuals with the same attributes as the entire population. As a result, the researcher gets a more accurate result without spending money and other necessary resources to survey a large and diverse population.
To ensure that this sampling method is more accessible and cost-effective, a researcher may include the following steps:
- They must define the target audience’s population size to make the sampling representative.
- The researcher must choose a sample size based on the total population to determine the number of individuals required to make the sample more precise.
- They must define the characteristic or attribute parameters they seek in the sample size. It is imperative to choose the right target audience and the appropriate sample group relevant to the study on which the research is being conducted.
How To Get?
Their methods to get a representative sample in statistics are as follows:
#1 – Probability Sampling
In this sampling technique, the researcher selects a smaller sample from a vast population using the probability theory method. First, the researcher must choose an individual using a random selection to consider them as probability sampling. Then, the researchers make a sample choice arbitrarily, ensuring that each member of the entire population has the same probability of being included in the sample group.
#2 – Non-Probability Sampling
In this sampling technique, the researcher selects samples based on their judgment instead of randomly picking them. In this sample type, each member of the total population may not get a chance to be included in the sample group. The researcher selects the sample size in such a manner that the individuals and the parameters on which they are chosen are relevant to the study. This method aims at collecting the most accurate data possible for successful research.
Examples
Let us understand the concept with the following examples:
Example #1
Suppose Jane wanted to study how many kids aged between 8 to 16 wish to engage in more than one co-curricular activity. To carry out this survey accurately, she needs to select a sample size that contains equal representatives from all the age groups between 8 to 16 years old. For example, if she chooses a more significant number of kids who are 10 years old and a smaller number of kids aged 14, the sample size will not reflect a fair representation. This is an example of a representative sample.
Example #2
An inquiry found that the pollsters failed to forecast the general election’s outcome in Britain due to inaccurate representative poll samples. The academics and statistician’s panel’s interim report suggested a systematic under-representation of Conservative voters and an over-representation of Labour Party voters. YouGov announced that they did not get access to enough individuals in the age group of seventy and older, who had more possibility to vote for the Conservative. This is an example of a representative sample.
Importance
The importance of a representative sample survey is as follows:
#1 – Effective And Practical
A representative sample survey helps in carrying out successful market research. Studying the entire population’s behavior would be time-consuming, complex, and require much time and resources. However, studying a small pool to determine the characteristics of the whole population helps provide accurate results without spending much time and money on surveying the total population.
#2 – Accurate Decision-Making
This sample type gives a fair representation of the total target audience. It provides the researcher with valuable insights into a vast audience pool. As a result, the decisions are not influenced by only a particular category of people. This ensures that the decisions made have greater accuracy.
#3 – Avoiding Sampling Errors
This sample ensures that the collected data is accurate and relevant to the study that is being conducted. This reduces the possibility of sampling errors and helps provide more precise results.
#4 – Originating A Good Return On Investment (ROI)
This sampling type helps understand the consumer’s perspective, which impacts the business decisions. It enables the researcher to select the appropriate audience and gain insights into their preferences. As a result, the products and services improve, leading to a better ROI.
Representative Sample vs Random Sample vs Biased Sample
The differences are as follows:
- Representative Sample: In this sample type, the representative sample size reflects the characteristics and attributes of the entire population. It is a small part of the total population that reflects the properties of the larger group.
- Random Sample: In this sampling type, the researcher randomly selects individuals from the total population. There are no conscious criteria for choosing the individuals. Each member from the large group has an equal possibility of being selected.
- Biased Sample: This sampling type is focused on studying a particular event or phenomenon. In this sampling type, the researcher prioritizes some attributes over others while choosing the sample size. It does not accurately represent the total population and, thus, may lead to skewed results.
Representative Sample vs Non-Representative Sample
The differences are as follows:
- Representative Sample: In this sampling type, the researcher deliberately chooses a representative sample size that represents some attribute of the entire population and demonstrates the properties of the total population. For example, if the researcher wants to study how accessible public washrooms are for people with disabilities, then the sample size will comprise disabled people.
- Non-Representative Sample: In this sample type, the researcher does not focus on any particular attribute or parameter from the entire population. The selected sample size accurately represents or demonstrates the properties of the total population.
Frequently Asked Questions (FAQs)
A minimum thirty percent ratio, or around 300 individuals for a population under 1,000, ensures the sample’s representativeness. On the other hand, for an enormous population, like a population of 100,000, a smaller minimum ratio of around ten percent, that is, 10,000 individuals, is needed to ensure the sample’s representativeness. However, in the case of this sample, the sample source should be given more priority over the sample size.
No, this sample is not biased. In a biased sample, one or more aspects of the population are prioritized over the others. However, in an unbiased sample, each individual from the population gets an equal possibility to be selected. This sample is a small part of the entire population that represents the traits of the larger group. Therefore, this sample must be unbiased for an accurate and fair result.
No, case studies usually do not provide this sample type. A robust case study does not need a representative or random sample. On the contrary, case studies generally focus on uncommon, obscure, or neglected cases that may provide a new perspective on the research problem. However, one can select a more conventional or representative topic to demonstrate a particular group, event, or occurrence.
Recommended Articles
This article has been a guide to what is Representative Sample. We explain it with its examples, importance, and comparison with random & non-representative samples. You may also find some useful articles here –
- Inferential Statistics
- Variance
- Decile
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