Simple random sampling occurs when a subset of a statistical population allows for each member of the demographic to have an equal opportunity of being chosen for surveys, polls, or research projects. The goal of collecting information in this way is to provide an unbiased representation of the entire group.
Investopedia uses the example of a simple random sample as having the names of 25 employees being chosen out of a hat from a company of 250 workers. In this example, the population would be the entire workforce, while the sample is random from the hat because every worker has an equal chance of being chosen every time a name is drawn.
When simple random sampling is used in the field of science, the goal is to conduct randomized controlled tests or create blinded experiments that can extract information from individuals which can then be useful for applying to the entire group. The random Ness of this process creates, under most circumstances, a balanced subset which carries the most potential for representing the larger group as a whole.
These are the advantages and disadvantages of simple random sampling you will want to consider when looking at the subjects.
List of the Advantages of Simple Random Sampling
1. It is easier to form representative groups from an overall population.
The random sampling process identifies individuals who belong to an overall population. This demographic is a reflection of the exact sample that researchers wish to interview or study. Because of the structure, it becomes much easier to form a sample group since the only work necessary is to identify the components of the targeted demographic.
Once that overall population is identified, the only work to do is to randomize which individuals or what circumstances will receive study.
2. It allows the findings to apply to the entire demographic.
Because researchers are randomly pulling individuals for a simple random sampling, there is a strong likelihood that the information received through this process will apply to the entire population. Although there are times when people will purposely misrepresent who they are because they are following their own agenda, this process typically produces accurate information within a small margin of error. Then researchers can use this data to apply their findings for everyone within the overall population.
3. It provides for multiple randomness types to be included in the research.
When you have a group of researchers pursuing information, then there is the possibility that a conscious or unconscious bias may apply to the application of this data. Simple random sampling reduces this risk by allowing for multiple types of randomness in the selection of the individuals or circumstances being studied. The most common option with this advantage is called the “lottery method.” It involves the population group being selected through a random draw, which could be done through the example of having everyone’s name in a hat.
The second option for this advantage utilizes random numbers which researchers can then assign to specific individuals or events. Then these numbers can be pulled without any foreknowledge of whom they represent, reducing the likelihood that those involved in the research will be going into the data collection process with preconceived notions.
4. It is the easiest method of data collection that is available for research.
Simple random sampling uses common recording skills and standard observation techniques to collect information. It does not require the individuals who are being studied to have specific skills or life experiences to create useful data. This process can also remove the classification errors that can occur in other forms of information collection. There can be some disadvantages because of the overall simplicity of this process, but it typically allows for a greater understanding on specific questions or needs without the costly processes of qualification that other research methods may mandate.
5. It request less experience and knowledge to complete the work.
Unlike other forms of research, the individuals involved with simple random sampling are not required to have industry-specific knowledge about the data points they work to collect. Think of this process as a journalistic interview. You can ask someone something about anything, and then record their answers. You would then repeat this process with multiple individuals to create comparable data points. Then you would look for patterns within the information that show trends, problems, solutions, or whatever else the researchers want to find within a specific population.
The only requirement necessary to be a researcher in simple random sampling is to have the ability to collect and record data.
6. It offers an equal chance of selection for everyone within the population group.
Have you ever watched a roulette wheel at a casino? Some tables come equipped with a board that shows the history of the numbers that have been recorded. The goal is to make the randomized numbers seem to have a pattern to them, encouraging people to visit the table with a bet. Repetitive numbers can happen, even if the odds are against it. Why this can happen is because there is an equal chance of selection as the wheel spins with the ball. The same process holds true for simple random sampling.
Researchers can build fairness within the data they collect because there is no foreknowledge of who will be part of the effort. That is why the information can apply to the entire population group. There is more fairness involved because there is always an equal chance for selection.
7. It provides information with a lower chance of data errors.
Simple random sampling offers researchers an opportunity to perform data analysis and a way that creates a lower margin of error within the information collected. This advantage occurs because the sampling structure happens within specific boundaries set to reflect population groups. Thanks to the randomization of selection, the entire population receives usable observations that can offer specific insights at the individual level even though a small group (and not the entire population) was surveyed or studied.
List of the Disadvantages of Simple Random Sampling
1. It relies on the quality of the researchers performing the work.
This disadvantage occurs frequently with simple random sampling because the skills of the researcher are necessary for information collection. If the work requires individual researchers to interview subjects in person, then the quality of the data relies on the ability to follow the structure of the study. Interviewers who fail to stick to a script or do not have the ability to follow up on ambiguous answers could create gaps in the information that would become a misrepresentation of the overall demographic.
Simple random sampling must endure the same overall disadvantage that every other form of research encounters: poor method application will also result in inferior information.
2. It can require a sample size that is too large.
Simple random sampling works best when you can manage a small percentage of the overall demographic. In the example used in the introduction for this piece, drawing names from the hat represent 10% of the total population. Larger groups require a more significant frame for the information collected so that it could be accurate. If researchers use a structure that is too small, then the margin of error will rise significantly, effectively rendering the data unusable. That is why the overall size of a survey is limited in its scope.
3. It must have a significant population or demographic at the beginning of the process.
If simple random sampling is going to be an effective research method, then there must be a significant population or demographic available to start the selection process. If you only have 10 people in a specific situation, surveying only two of them will not give you an accurate representation of how everyone feels in many circumstances. There must be a larger size available to use this method. Smaller groups work better when researchers include everyone in the sample because that process gives you a complete look at the situation instead of only a partial one.
4. It does not provide a guarantee that the data conclusions will be accurate.
A simple random sampling is the preference for many researchers because the process reduces the risk of bias or inaccuracy within the data being collected. When you are pulling a small group out of an overall demographic to determine what they think or feel, then there is no guarantee that the information will be a reflection of how everyone else perceives the situation. Although the margin of error is typically lower with this process, you could also end up with inaccurate results because the random pulling managed to include more weight on one side of the equation than the other.
5. It does not work well with widely diverse or dispersed population groups.
This research method works well when the demographics being studied all fall into a similar geographic situation, employment pattern, and household structure. When there are numerous differences in household income, culture, ethnicity, and even race, the different perspectives that each individual encounters every day can skew the data that researchers collect. It is imperative that small groupings occur in situations where the overall population is scattered and diverse to ensure that the margin of error stays within an appropriate level.
6. It can require additional monetary investments when compared to other methods.
Researchers use simple random sampling because the data collection methods used for this process are fast and easy to implement. Because the research occurs at the individual level when choosing this option, there is another cost component to it that must receive consideration. Random samples tend to be more expensive than other research methods because you must have one-on-one interactions with the people involved. Follow-up conversations, interviews, or surveys are sometimes necessary to validate the answers given as well.
The information that this sampling offers is accurate and valuable to those who use the data to solve the problems of the overall population, but there are times when the actual expense of the work can outweigh the potential profits that are possible.
7. It cannot remove intentional bias from the data collection process.
Simple random sampling is effective because of how its structure can limit the influence of an unconscious bias. What does process cannot achieve is a limitation of intentionally influenced data from researchers or participants who wish to create a specific result that benefits their own needs in some way. Researchers can choose people from a specific geographic region because they know that there is a greater likelihood that the results will be favorable for the outcome as they wish to see.
There is also no guarantee that the participants in a simple random sampling will provide authentic information for use. Respondents tend to lie when given a survey online. Up to 50% in any given sample, including random ones, will provide dishonest responses. People will create this disadvantage because they are defensive about the questions posed to them, wish to be socially accepted, or are trying to be polite because they don’t want to be offensive with their answer.
8. It cannot predict the future because individuals can change their mind frequently.
The information researchers collect with a simple random sampling cam provide an accurate representation of an overall population, but it provides accuracy for a brief window of time. No one can predict the future, and some people can change their minds frequently about specific issues. 23% of people between the ages of 18 to 29 say that information that they discovered on social media change their mind about how they felt or an opinion they held. Even in the 65 and older category, 6% said that they changed their mind because of something they saw online. That means the information you gather today through this process might not be accurate tomorrow.
9. It can be a time-consuming process to conduct this research.
Researchers must include every person or circumstance selected through the random sampling process to complete the work. If the size of the sampling is significant, then this research process can take a significant amount of time to complete. You must avoid speaking with people in groups for the data to be accurate because individuals tend to shift their answers when they want to get along with others or feel threatened if their response is different. That means this process requires one-on-one conversations, requiring a massive time commitment for each step.
10. It requests expertise in data collection methods.
Researchers using this method may not need to hold industry-specific experience to produce results, but they do need to have information collection experience to be effective at what they do each day. It is up to each researcher to determine if the data they collect is accurate or not. They are the guardians of authenticity in results generation as well, which means there must be an understanding of what each observable point represents to the overall population. If this disadvantage is present, then there is no guarantee that the published findings are accurate, even if the data itself was collected without bias.
11. It may not start with the entire population.
The entire population or demographic must be included with the randomization process for the information collected by researchers to be accurate. Because many groups can have an extensive size, having a full list of each person who could be randomly drawn may be impossible. Although you can sometimes access details on specific demographics within an institution, this disadvantage still applies because the individual perspectives are more important to the results than statistical data. Without the entire group, there is no way to extrapolate the results from a subset to everyone else.
One Final Thought on Simple Random Sampling
If you have ever looked at survey results on a news broadcast, then you might have thought that the information provided was in accurate because the results are not a reflection of who you are. A misrepresentation of the overall population can doom simple random sampling before the work ever begins. You can tell how effective the process was on reported results because a more inclusive study will drop the margin of error to 2% to 3%. If you see 5% or higher, there is little evidence to assume that the data is accurate.
When we take a look at the advantages and disadvantages of simple random sampling, then we can see how a correctly structured research project can provide accurate information. It removes the bias of the researcher without adversely impacting the quality of the data being collected through this process. We can then analyze the results, look for patterns, and expand the conclusions to reach the entire population.
There must be controls in place for this work to be beneficial. If we can plan for potential problems before the research starts, then simple random sampling is an easy, straightforward method of collecting data. If we cannot accomplish this result, then the information could be useless.
Natalie Regoli is our editor-in-chief. The goal of ConnectUs is to publish compelling content that addresses some of the biggest issues the world faces. If you would like to reach out to contact Natalie, then go here to send her a message.