random assignment sampling example

Random Assignment in Psychology: Definition & Examples

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In psychology, random assignment refers to the practice of allocating participants to different experimental groups in a study in a completely unbiased way, ensuring each participant has an equal chance of being assigned to any group.

In experimental research, random assignment, or random placement, organizes participants from your sample into different groups using randomization. 

Random assignment uses chance procedures to ensure that each participant has an equal opportunity of being assigned to either a control or experimental group.

The control group does not receive the treatment in question, whereas the experimental group does receive the treatment.

When using random assignment, neither the researcher nor the participant can choose the group to which the participant is assigned. This ensures that any differences between and within the groups are not systematic at the onset of the study. 

In a study to test the success of a weight-loss program, investigators randomly assigned a pool of participants to one of two groups.

Group A participants participated in the weight-loss program for 10 weeks and took a class where they learned about the benefits of healthy eating and exercise.

Group B participants read a 200-page book that explains the benefits of weight loss. The investigator randomly assigned participants to one of the two groups.

The researchers found that those who participated in the program and took the class were more likely to lose weight than those in the other group that received only the book.

Importance 

Random assignment ensures that each group in the experiment is identical before applying the independent variable.

In experiments , researchers will manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables. Random assignment increases the likelihood that the treatment groups are the same at the onset of a study.

Thus, any changes that result from the independent variable can be assumed to be a result of the treatment of interest. This is particularly important for eliminating sources of bias and strengthening the internal validity of an experiment.

Random assignment is the best method for inferring a causal relationship between a treatment and an outcome.

Random Selection vs. Random Assignment 

Random selection (also called probability sampling or random sampling) is a way of randomly selecting members of a population to be included in your study.

On the other hand, random assignment is a way of sorting the sample participants into control and treatment groups. 

Random selection ensures that everyone in the population has an equal chance of being selected for the study. Once the pool of participants has been chosen, experimenters use random assignment to assign participants into groups. 

Random assignment is only used in between-subjects experimental designs, while random selection can be used in a variety of study designs.

Random Assignment vs Random Sampling

Random sampling refers to selecting participants from a population so that each individual has an equal chance of being chosen. This method enhances the representativeness of the sample.

Random assignment, on the other hand, is used in experimental designs once participants are selected. It involves allocating these participants to different experimental groups or conditions randomly.

This helps ensure that any differences in results across groups are due to manipulating the independent variable, not preexisting differences among participants.

When to Use Random Assignment

Random assignment is used in experiments with a between-groups or independent measures design.

In these research designs, researchers will manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables.

There is usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable at the onset of the study.

How to Use Random Assignment

There are a variety of ways to assign participants into study groups randomly. Here are a handful of popular methods: 

• Random Number Generator : Give each member of the sample a unique number; use a computer program to randomly generate a number from the list for each group.
• Lottery : Give each member of the sample a unique number. Place all numbers in a hat or bucket and draw numbers at random for each group.
• Flipping a Coin : Flip a coin for each participant to decide if they will be in the control group or experimental group (this method can only be used when you have just two groups) 
• Roll a Die : For each number on the list, roll a dice to decide which of the groups they will be in. For example, assume that rolling 1, 2, or 3 places them in a control group and rolling 3, 4, 5 lands them in an experimental group.

When is Random Assignment not used?

• When it is not ethically permissible: Randomization is only ethical if the researcher has no evidence that one treatment is superior to the other or that one treatment might have harmful side effects. 
• When answering non-causal questions : If the researcher is just interested in predicting the probability of an event, the causal relationship between the variables is not important and observational designs would be more suitable than random assignment. 
• When studying the effect of variables that cannot be manipulated: Some risk factors cannot be manipulated and so it would not make any sense to study them in a randomized trial. For example, we cannot randomly assign participants into categories based on age, gender, or genetic factors.

Drawbacks of Random Assignment

While randomization assures an unbiased assignment of participants to groups, it does not guarantee the equality of these groups. There could still be extraneous variables that differ between groups or group differences that arise from chance. Additionally, there is still an element of luck with random assignments.

Thus, researchers can not produce perfectly equal groups for each specific study. Differences between the treatment group and control group might still exist, and the results of a randomized trial may sometimes be wrong, but this is absolutely okay.

Scientific evidence is a long and continuous process, and the groups will tend to be equal in the long run when data is aggregated in a meta-analysis.

Additionally, external validity (i.e., the extent to which the researcher can use the results of the study to generalize to the larger population) is compromised with random assignment.

Random assignment is challenging to implement outside of controlled laboratory conditions and might not represent what would happen in the real world at the population level. 

Random assignment can also be more costly than simple observational studies, where an investigator is just observing events without intervening with the population.

Randomization also can be time-consuming and challenging, especially when participants refuse to receive the assigned treatment or do not adhere to recommendations. 

What is the difference between random sampling and random assignment?

Random sampling refers to randomly selecting a sample of participants from a population. Random assignment refers to randomly assigning participants to treatment groups from the selected sample.

Does random assignment increase internal validity?

Yes, random assignment ensures that there are no systematic differences between the participants in each group, enhancing the study’s internal validity .

Does random assignment reduce sampling error?

Yes, with random assignment, participants have an equal chance of being assigned to either a control group or an experimental group, resulting in a sample that is, in theory, representative of the population.

Random assignment does not completely eliminate sampling error because a sample only approximates the population from which it is drawn. However, random sampling is a way to minimize sampling errors. 

When is random assignment not possible?

Random assignment is not possible when the experimenters cannot control the treatment or independent variable.

For example, if you want to compare how men and women perform on a test, you cannot randomly assign subjects to these groups.

Participants are not randomly assigned to different groups in this study, but instead assigned based on their characteristics.

Does random assignment eliminate confounding variables?

Yes, random assignment eliminates the influence of any confounding variables on the treatment because it distributes them at random among the study groups. Randomization invalidates any relationship between a confounding variable and the treatment.

Why is random assignment of participants to treatment conditions in an experiment used?

Random assignment is used to ensure that all groups are comparable at the start of a study. This allows researchers to conclude that the outcomes of the study can be attributed to the intervention at hand and to rule out alternative explanations for study results.

Further Reading

• Bogomolnaia, A., & Moulin, H. (2001). A new solution to the random assignment problem .  Journal of Economic theory ,  100 (2), 295-328.
• Krause, M. S., & Howard, K. I. (2003). What random assignment does and does not do .  Journal of Clinical Psychology ,  59 (7), 751-766.

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AP®︎/College Statistics

Course: ap®︎/college statistics   >   unit 6.

• Statistical significance of experiment

Random sampling vs. random assignment (scope of inference)

• Conclusions in observational studies versus experiments
• Finding errors in study conclusions
• (Choice A)   Just the residents involved in Hilary's study. A Just the residents involved in Hilary's study.
• (Choice B)   All residents in Hilary's town. B All residents in Hilary's town.
• (Choice C)   All residents in Hilary's country. C All residents in Hilary's country.
• (Choice A)   Yes A Yes
• (Choice B)   No B No
• (Choice A)   Just the residents in Hilary's study. A Just the residents in Hilary's study.

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15 Random Assignment Examples

In research, random assignment refers to the process of randomly assigning research participants into groups (conditions) in order to minimize the influence of confounding variables or extraneous factors .

Ideally, through randomization, each research participant has an equal chance of ending up in either the control or treatment condition group.

For example, consider the following two groups under analysis. Under a model such as self-selection or snowball sampling, there may be a chance that the reds cluster themselves into one group (The reason for this would likely be that there is a confounding variable that the researchers have not controlled for):

To maximize the chances that the reds will be evenly split between groups, we could employ a random assignment method, which might produce the following more balanced outcome:

This process is considered a gold standard for experimental research and is generally expected of major studies that explore the effects of independent variables on dependent variables .

However, random assignment is not without its flaws – chief among them being the importance of a sufficiently sized sample which will allow for randomization to tend toward a mean (take, for example, the odds of 50/50 heads and tail after 100 coin flips being higher than 1/1 heads and tail after 2 coin flips). In fact, even in the above example where I randomized the colors, you can see that there are twice as many yellows in the treatment condition than the control condition, likely because of the low number of research participants.

Methods for Random Assignment of Participants

Randomly assigning research participants into controls is relatively easy. However, there is a range of ways to go about it, and each method has its own pros and cons.

For example, there are some strategies – like the matched-pair method – that can help you to control for confounds in interesting ways.

Here are some of the most common methods of random assignment, with explanations of when you might want to use each one:

1. Simple Random Assignment This is the most basic form of random assignment. All participants are pooled together and then divided randomly into groups using an equivalent chance process such as flipping a coin, drawing names from a hat, or using a random number generator. This method is straightforward and ensures each participant has an equal chance of being assigned to any group (Jamison, 2019; Nestor & Schutt, 2018).

2. Block Randomization In this method, the researcher divides the participants into “blocks” or batches of a pre-determined size, which is then randomized (Alferes, 2012). This technique ensures that the researcher will have evenly sized groups by the end of the randomization process. It’s especially useful in clinical trials where balanced and similar-sized groups are vital.

3. Stratified Random Assignment In stratified random assignment, the researcher categorizes the participants based on key characteristics (such as gender, age, ethnicity) before the random allocation process begins. Each stratum is then subjected to simple random assignment. This method is beneficial when the researcher aims to ensure that the groups are balanced with regard to certain characteristics or variables (Rosenberger & Lachin, 2015).

4. Cluster Random Assignment Here, pre-existing groups or clusters, such as schools, households, or communities, are randomly assigned to different conditions of a research study. It’s ideal when individual random assignment is not feasible, or when the treatment is naturally delivered at the group or community level (Blair, Coppock & Humphreys, 2023).

5. Matched-Pair Random Assignment In this method, participants are first paired based on a particular characteristic or set of characteristics that are relevant to the research study, such as age, gender, or a specific health condition. Each pair is then split randomly into different research conditions or groups. This can help control for the influence of specific variables and increase the likelihood that the groups will be comparable, thereby increasing the validity of the results (Nestor & Schutt, 2018).

Random Assignment Examples

1. Pharmaceutical Efficacy Study In this type of research, consider a scenario where a pharmaceutical company wishes to test the potency of two different versions of a medication, Medication A and Medication B. The researcher recruits a group of volunteers and randomly assigns them to receive either Medication A or Medication B. This method ensures that each participant has an equal chance of being given either option, mitigating potential bias from the investigator’s side. It’s an expectation, for example, for FDA approval pre-trials (Rosenberger & Lachin, 2015).

2. Educational Techniques Study In this approach, an educator looking to evaluate a new teaching technique may randomly assign their students into two distinct classrooms. In one classroom, the new teaching technique will be implemented, while in the other, traditional methods will be utilized. The students’ performance will then be analyzed to determine if the new teaching strategy yields better results. To ensure the class cohorts are randomly assigned, we need to make sure there is no interference from parents, administrators, or others.

3. Website Usability Test In this digital-oriented example, a web designer could be researching the most effective layout for a website. Participants would be randomly assigned to use websites with a different layout and their navigation and satisfaction would be subsequently measured. This technique helps identify which design is user-friendlier based on the measured outcomes.

4. Physical Fitness Research For an investigator looking to evaluate the effectiveness of different exercise routines for weight loss, they could randomly assign participants to either a High-Intensity Interval Training (HIIT) or an endurance-based running program. By studying the participants’ weight changes across a specified time, a conclusion can be drawn on which exercise regime produces better weight loss results.

5. Environmental Psychology Study In this illustration, imagine a psychologist wanting to understand how office settings influence employees’ productivity. He could randomly assign employees to work in one of two offices: one with windows and natural light, the other windowless. The psychologist would then measure their work output to gauge if the environmental conditions impact productivity.

6. Dietary Research Test In this case, a dietician, striving to determine the efficacy of two diets on heart health, might randomly assign participants to adhere to either a Mediterranean diet or a low-fat diet. The dietician would then track cholesterol levels, blood pressure, and other heart health indicators over a determined period to discern which diet benefits heart health the most.

7. Mental Health Study In examining the IMPACT (Improving Mood-Promoting Access to Collaborative Treatment) model, a mental health researcher could randomly assign patients to receive either standard depression treatment or the IMPACT model treatment. Here, the purpose is to cross-compare recovery rates to gauge the effectiveness of the IMPACT model against the standard treatment.

8. Marketing Research A company intending to validate the effectiveness of different marketing strategies could randomly assign customers to receive either email marketing materials or social media marketing materials. Customer response and engagement rates would then be measured to evaluate which strategy is more beneficial and drives better engagement.

9. Sleep Study Research Suppose a researcher wants to investigate the effects of different levels of screen time on sleep quality. The researcher may randomly assign participants to varying amounts of nightly screen time, then compare sleep quality metrics (such as total sleep time, sleep latency, and awakenings during the night).

10. Workplace Productivity Experiment Let’s consider an HR professional who aims to evaluate the efficacy of open office and closed office layouts on employee productivity. She could randomly assign a group of employees to work in either environment and measure metrics such as work completed, attention to detail, and number of errors made to determine which office layout promotes higher productivity.

11. Child Development Study Suppose a developmental psychologist wants to investigate the effect of different learning tools on children’s development. The psychologist could randomly assign children to use either digital learning tools or traditional physical learning tools, such as books, for a fixed period. Subsequently, their development and learning progression would be tracked to determine which tool fosters more effective learning.

12. Traffic Management Research In an urban planning study, researchers could randomly assign streets to implement either traditional stop signs or roundabouts. The researchers, over a predetermined period, could then measure accident rates, traffic flow, and average travel times to identify which traffic management method is safer and more efficient.

13. Energy Consumption Study In a research project comparing the effectiveness of various energy-saving strategies, residents could be randomly assigned to implement either energy-saving light bulbs or regular bulbs in their homes. After a specific duration, their energy consumption would be compared to evaluate which measure yields better energy conservation.

14. Product Testing Research In a consumer goods case, a company looking to launch a new dishwashing detergent could randomly assign the new product or the existing best seller to a group of consumers. By analyzing their feedback on cleaning capabilities, scent, and product usage, the company can find out if the new detergent is an improvement over the existing one Nestor & Schutt, 2018.

15. Physical Therapy Research A physical therapist might be interested in comparing the effectiveness of different treatment regimens for patients with lower back pain. They could randomly assign patients to undergo either manual therapy or exercise therapy for a set duration and later evaluate pain levels and mobility.

Random assignment is effective, but not infallible. Nevertheless, it does help us to achieve greater control over our experiments and minimize the chances that confounding variables are undermining the direct correlation between independent and dependent variables within a study. Over time, when a sufficient number of high-quality and well-designed studies are conducted, with sufficient sample sizes and sufficient generalizability, we can gain greater confidence in the causation between a treatment and its effects.

Read Next: Types of Research Design

Alferes, V. R. (2012). Methods of randomization in experimental design . Sage Publications.

Blair, G., Coppock, A., & Humphreys, M. (2023). Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign. New Jersey: Princeton University Press.

Jamison, J. C. (2019). The entry of randomized assignment into the social sciences. Journal of Causal Inference , 7 (1), 20170025.

Nestor, P. G., & Schutt, R. K. (2018). Research Methods in Psychology: Investigating Human Behavior. New York: SAGE Publications.

Rosenberger, W. F., & Lachin, J. M. (2015). Randomization in Clinical Trials: Theory and Practice. London: Wiley.

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What Is Random Assignment in Psychology?

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Random assignment means that every participant has the same chance of being chosen for the experimental or control group. It involves using procedures that rely on chance to assign participants to groups. Doing this means that every participant in a study has an equal opportunity to be assigned to any group.

For example, in a psychology experiment, participants might be assigned to either a control or experimental group. Some experiments might only have one experimental group, while others may have several treatment variations.

Using random assignment means that each participant has the same chance of being assigned to any of these groups.

Table of Contents

How to Use Random Assignment

So what type of procedures might psychologists utilize for random assignment? Strategies can include:

• Flipping a coin
• Assigning random numbers
• Rolling dice
• Drawing names out of a hat

How Does Random Assignment Work?

A psychology experiment aims to determine if changes in one variable lead to changes in another variable. Researchers will first begin by coming up with a hypothesis. Once researchers have an idea of what they think they might find in a population, they will come up with an experimental design and then recruit participants for their study.

Once they have a pool of participants representative of the population they are interested in looking at, they will randomly assign the participants to their groups.

• Control group : Some participants will end up in the control group, which serves as a baseline and does not receive the independent variables.
• Experimental group : Other participants will end up in the experimental groups that receive some form of the independent variables.

By using random assignment, the researchers make it more likely that the groups are equal at the start of the experiment. Since the groups are the same on other variables, it can be assumed that any changes that occur are the result of varying the independent variables.

After a treatment has been administered, the researchers will then collect data in order to determine if the independent variable had any impact on the dependent variable.

Random Assignment vs. Random Selection

It is important to remember that random assignment is not the same thing as random selection , also known as random sampling.

Random selection instead involves how people are chosen to be in a study. Using random selection, every member of a population stands an equal chance of being chosen for a study or experiment.

So random sampling affects how participants are chosen for a study, while random assignment affects how participants are then assigned to groups.

Examples of Random Assignment

Imagine that a psychology researcher is conducting an experiment to determine if getting adequate sleep the night before an exam results in better test scores.

Forming a Hypothesis

They hypothesize that participants who get 8 hours of sleep will do better on a math exam than participants who only get 4 hours of sleep.

Obtaining Participants

The researcher starts by obtaining a pool of participants. They find 100 participants from a local university. Half of the participants are female, and half are male.

Randomly Assign Participants to Groups

The researcher then assigns random numbers to each participant and uses a random number generator to randomly assign each number to either the 4-hour or 8-hour sleep groups.

Conduct the Experiment

Those in the 8-hour sleep group agree to sleep for 8 hours that night, while those in the 4-hour group agree to wake up after only 4 hours. The following day, all of the participants meet in a classroom.

Collect and Analyze Data

Everyone takes the same math test. The test scores are then compared to see if the amount of sleep the night before had any impact on test scores.

Why Is Random Assignment Important in Psychology Research?

Random assignment is important in psychology research because it helps improve a study’s internal validity. This means that the researchers are sure that the study demonstrates a cause-and-effect relationship between an independent and dependent variable.

Random assignment improves the internal validity by minimizing the risk that there are systematic differences in the participants who are in each group.

Key Points to Remember About Random Assignment

• Random assignment in psychology involves each participant having an equal chance of being chosen for any of the groups, including the control and experimental groups.
• It helps control for potential confounding variables, reducing the likelihood of pre-existing differences between groups.
• This method enhances the internal validity of experiments, allowing researchers to draw more reliable conclusions about cause-and-effect relationships.
• Random assignment is crucial for creating comparable groups and increasing the scientific rigor of psychological studies.

Statistics Made Easy

Random Selection vs. Random Assignment

Random selection and random assignment  are two techniques in statistics that are commonly used, but are commonly confused.

Random selection  refers to the process of randomly selecting individuals from a population to be involved in a study.

Random assignment  refers to the process of randomly  assigning  the individuals in a study to either a treatment group or a control group.

You can think of random selection as the process you use to “get” the individuals in a study and you can think of random assignment as what you “do” with those individuals once they’re selected to be part of the study.

The Importance of Random Selection and Random Assignment

When a study uses  random selection , it selects individuals from a population using some random process. For example, if some population has 1,000 individuals then we might use a computer to randomly select 100 of those individuals from a database. This means that each individual is equally likely to be selected to be part of the study, which increases the chances that we will obtain a representative sample – a sample that has similar characteristics to the overall population.

By using a representative sample in our study, we’re able to generalize the findings of our study to the population. In statistical terms, this is referred to as having  external validity – it’s valid to externalize our findings to the overall population.

When a study uses  random assignment , it randomly assigns individuals to either a treatment group or a control group. For example, if we have 100 individuals in a study then we might use a random number generator to randomly assign 50 individuals to a control group and 50 individuals to a treatment group.

By using random assignment, we increase the chances that the two groups will have roughly similar characteristics, which means that any difference we observe between the two groups can be attributed to the treatment. This means the study has  internal validity  – it’s valid to attribute any differences between the groups to the treatment itself as opposed to differences between the individuals in the groups.

Examples of Random Selection and Random Assignment

It’s possible for a study to use both random selection and random assignment, or just one of these techniques, or neither technique. A strong study is one that uses both techniques.

The following examples show how a study could use both, one, or neither of these techniques, along with the effects of doing so.

Example 1: Using both Random Selection and Random Assignment

Study:  Researchers want to know whether a new diet leads to more weight loss than a standard diet in a certain community of 10,000 people. They recruit 100 individuals to be in the study by using a computer to randomly select 100 names from a database. Once they have the 100 individuals, they once again use a computer to randomly assign 50 of the individuals to a control group (e.g. stick with their standard diet) and 50 individuals to a treatment group (e.g. follow the new diet). They record the total weight loss of each individual after one month.

Results:  The researchers used random selection to obtain their sample and random assignment when putting individuals in either a treatment or control group. By doing so, they’re able to generalize the findings from the study to the overall population  and  they’re able to attribute any differences in average weight loss between the two groups to the new diet.

Example 2: Using only Random Selection

Study:  Researchers want to know whether a new diet leads to more weight loss than a standard diet in a certain community of 10,000 people. They recruit 100 individuals to be in the study by using a computer to randomly select 100 names from a database. However, they decide to assign individuals to groups based solely on gender. Females are assigned to the control group and males are assigned to the treatment group. They record the total weight loss of each individual after one month.

Results:  The researchers used random selection to obtain their sample, but they did not use random assignment when putting individuals in either a treatment or control group. Instead, they used a specific factor – gender – to decide which group to assign individuals to. By doing this, they’re able to generalize the findings from the study to the overall population but they are  not  able to attribute any differences in average weight loss between the two groups to the new diet. The internal validity of the study has been compromised because the difference in weight loss could actually just be due to gender, rather than the new diet.

Example 3: Using only Random Assignment

Study:  Researchers want to know whether a new diet leads to more weight loss than a standard diet in a certain community of 10,000 people. They recruit 100 males athletes to be in the study. Then, they use a computer program to randomly assign 50 of the male athletes to a control group and 50 to the treatment group. They record the total weight loss of each individual after one month.

Results:  The researchers did not use random selection to obtain their sample since they specifically chose 100 male athletes. Because of this, their sample is not representative of the overall population so their external validity is compromised – they will not be able to generalize the findings from the study to the overall population. However, they did use random assignment, which means they can attribute any difference in weight loss to the new diet.

Example 4: Using Neither Technique

Study:  Researchers want to know whether a new diet leads to more weight loss than a standard diet in a certain community of 10,000 people. They recruit 50 males athletes and 50 female athletes to be in the study. Then, they assign all of the female athletes to the control group and all of the male athletes to the treatment group. They record the total weight loss of each individual after one month.

Results:  The researchers did not use random selection to obtain their sample since they specifically chose 100 athletes. Because of this, their sample is not representative of the overall population so their external validity is compromised – they will not be able to generalize the findings from the study to the overall population. Also, they split individuals into groups based on gender rather than using random assignment, which means their internal validity is also compromised – differences in weight loss might be due to gender rather than the diet.

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Random Assignment – A Simple Introduction with Examples

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Completing a research or thesis paper is more work than most students imagine. For instance, you must conduct experiments before coming up with conclusions. Random assignment, a key methodology in academic research, ensures every participant has an equal chance of being placed in any group within an experiment. In experimental studies, the random assignment of participants is a vital element, which this article will discuss.

Inhaltsverzeichnis

• 1 Random Assignment – In a Nutshell
• 2 Definition: Random assignment
• 3 Importance of random assignment
• 4 Random assignment vs. random sampling
• 5 How to use random assignment
• 6 When random assignment is not used

Random Assignment – In a Nutshell

• Random assignment is where you randomly place research participants into specific groups.
• This method eliminates bias in the results by ensuring that all participants have an equal chance of getting into either group.
• Random assignment is usually used in independent measures or between-group experiment designs.

Definition: Random assignment

Pearson Correlation is a descriptive statistical procedure that describes the measure of linear dependence between two variables. It entails a sample, control group , experimental design , and randomized design. In this statistical procedure, random assignment is used. Random assignment is the random placement of participants into different groups in experimental research.

Importance of random assignment

Random assessment is essential for strengthening the internal validity of experimental research. Internal validity helps make a casual relationship’s conclusions reliable and trustworthy.

In experimental research, researchers isolate independent variables and manipulate them as they assess the impact while managing other variables. To achieve this, an independent variable for diverse member groups is vital. This experimental design is called an independent or between-group design.

Example: Different levels of independent variables

• In a medical study, you can research the impact of nutrient supplements on the immune (nutrient supplements = independent variable, immune = dependent variable)

Three independent participant levels are applicable here:

• Control group (given 0 dosages of iron supplements)
• The experimental group (low dosage)
• The second experimental group (high dosage)

This assignment technique in experiments ensures no bias in the treatment sets at the beginning of the trials. Therefore, if you do not use this technique, you won’t be able to exclude any alternate clarifications for your findings.

In the research experiment above, you can recruit participants randomly by handing out flyers at public spaces like gyms, cafés, and community centers. Then:

• Place the group from cafés in the control group
• Community center group in the low prescription trial group
• Gym group in the high-prescription group

Even with random participant assignment, other extraneous variables may still create bias in experiment results. However, these variations are usually low, hence should not hinder your research. Therefore, using random placement in experiments is highly necessary, especially where it is ethically required or makes sense for your research subject.

Random assignment vs. random sampling

Simple random sampling is a method of choosing the participants for a study. On the other hand, the random assignment involves sorting the participants selected through random sampling. Another difference between random sampling and random assignment is that the former is used in several types of studies, while the latter is only applied in between-subject experimental designs.

Your study researches the impact of technology on productivity in a specific company.

In such a case, you have contact with the entire staff. So, you can assign each employee a quantity and apply a random number generator to pick a specific sample.

For instance, from 500 employees, you can pick 200. So, the full sample is 200.

Random sampling enhances external validity, as it guarantees that the study sample is unbiased, and that an entire population is represented. This way, you can conclude that the results of your studies can be accredited to the autonomous variable.

After determining the full sample, you can break it down into two groups using random assignment. In this case, the groups are:

• The control group (does get access to technology)
• The experimental group (gets access to technology)

Using random assignment assures you that any differences in the productivity results for each group are not biased and will help the company make a decision.

How to use random assignment

Firstly, give each participant a unique number as an identifier. Then, use a specific tool to simplify assigning the participants to the sample groups. Some tools you can use are:

Random member assignment is a prevailing technique for placing participants in specific groups because each person has a fair opportunity of being put in either group.

Random assignment in block experimental designs

In complex experimental designs , you must group your participants into blocks before using the random assignment technique.

You can create participant blocks depending on demographic variables, working hours, or scores. However, the blocks imply that you will require a bigger sample to attain high statistical power.

After grouping the participants in blocks, you can use random assignments inside each block to allocate the members to a specific treatment condition. Doing this will help you examine if quality impacts the result of the treatment.

Depending on their unique characteristics, you can also use blocking in experimental matched designs before matching the participants in each block. Then, you can randomly allot each partaker to one of the treatments in the research and examine the results.

When random assignment is not used

As powerful a tool as it is, random assignment does not apply in all situations. Like the following:

Comparing different groups

When the purpose of your study is to assess the differences between the participants, random member assignment may not work.

If you want to compare teens and the elderly with and without specific health conditions, you must ensure that the participants have specific characteristics. Therefore, you cannot pick them randomly.

In such a study, the medical condition (quality of interest) is the independent variable, and the participants are grouped based on their ages (different levels). Also, all partakers are tried similarly to ensure they have the medical condition, and their outcomes are tested per group level.

No ethical justifiability

Another situation where you cannot use random assignment is if it is ethically not permitted.

If your study involves unhealthy or dangerous behaviors or subjects, such as drug use. Instead of assigning random partakers to sets, you can conduct quasi-experimental research.

When using a quasi-experimental design , you examine the conclusions of pre-existing groups you have no control over, such as existing drug users. While you cannot randomly assign them to groups, you can use variables like their age, years of drug use, or socioeconomic status to group the participants.

What is the definition of random assignment?

It is an experimental research technique that involves randomly placing participants from your samples into different groups. It ensures that every sample member has the same opportunity of being in whichever group (control or experimental group).

When is random assignment applicable?

You can use this placement technique in experiments featuring an independent measures design. It helps ensure that all your sample groups are comparable.

What is the importance of random assignment?

It can help you enhance your study’s validity . This technique also helps ensure that every sample has an equal opportunity of being assigned to a control or trial group.

When should you NOT use random assignment

You should not use this technique if your study focuses on group comparisons or if it is not legally ethical.

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From around the world.

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We're sorry, but some features of Research Randomizer require JavaScript. If you cannot enable JavaScript, we suggest you use an alternative random number generator such as the one available at Random.org .

RESEARCH RANDOMIZER

Random sampling and random assignment made easy.

Research Randomizer is a free resource for researchers and students in need of a quick way to generate random numbers or assign participants to experimental conditions. This site can be used for a variety of purposes, including psychology experiments, medical trials, and survey research.

GENERATE NUMBERS

In some cases, you may wish to generate more than one set of numbers at a time (e.g., when randomly assigning people to experimental conditions in a "blocked" research design). If you wish to generate multiple sets of random numbers, simply enter the number of sets you want, and Research Randomizer will display all sets in the results.

Specify how many numbers you want Research Randomizer to generate in each set. For example, a request for 5 numbers might yield the following set of random numbers: 2, 17, 23, 42, 50.

Specify the lowest and highest value of the numbers you want to generate. For example, a range of 1 up to 50 would only generate random numbers between 1 and 50 (e.g., 2, 17, 23, 42, 50). Enter the lowest number you want in the "From" field and the highest number you want in the "To" field.

Selecting "Yes" means that any particular number will appear only once in a given set (e.g., 2, 17, 23, 42, 50). Selecting "No" means that numbers may repeat within a given set (e.g., 2, 17, 17, 42, 50). Please note: Numbers will remain unique only within a single set, not across multiple sets. If you request multiple sets, any particular number in Set 1 may still show up again in Set 2.

Sorting your numbers can be helpful if you are performing random sampling, but it is not desirable if you are performing random assignment. To learn more about the difference between random sampling and random assignment, please see the Research Randomizer Quick Tutorial.

Place Markers let you know where in the sequence a particular random number falls (by marking it with a small number immediately to the left). Examples: With Place Markers Off, your results will look something like this: Set #1: 2, 17, 23, 42, 50 Set #2: 5, 3, 42, 18, 20 This is the default layout Research Randomizer uses. With Place Markers Within, your results will look something like this: Set #1: p1=2, p2=17, p3=23, p4=42, p5=50 Set #2: p1=5, p2=3, p3=42, p4=18, p5=20 This layout allows you to know instantly that the number 23 is the third number in Set #1, whereas the number 18 is the fourth number in Set #2. Notice that with this option, the Place Markers begin again at p1 in each set. With Place Markers Across, your results will look something like this: Set #1: p1=2, p2=17, p3=23, p4=42, p5=50 Set #2: p6=5, p7=3, p8=42, p9=18, p10=20 This layout allows you to know that 23 is the third number in the sequence, and 18 is the ninth number over both sets. As discussed in the Quick Tutorial, this option is especially helpful for doing random assignment by blocks.

Please note: By using this service, you agree to abide by the SPN User Policy and to hold Research Randomizer and its staff harmless in the event that you experience a problem with the program or its results. Although every effort has been made to develop a useful means of generating random numbers, Research Randomizer and its staff do not guarantee the quality or randomness of numbers generated. Any use to which these numbers are put remains the sole responsibility of the user who generated them.

Note: By using Research Randomizer, you agree to its Terms of Service .

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The Random Selection Experiment Method

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

When researchers need to select a representative sample from a larger population, they often utilize a method known as random selection. In this selection process, each member of a group stands an equal chance of being chosen as a participant in the study.

Random Selection vs. Random Assignment

How does random selection differ from  random assignment ? Random selection refers to how the sample is drawn from the population as a whole, whereas random assignment refers to how the participants are then assigned to either the experimental or control groups.

It is possible to have both random selection and random assignment in an experiment.

Imagine that you use random selection to draw 500 people from a population to participate in your study. You then use random assignment to assign 250 of your participants to a control group (the group that does not receive the treatment or independent variable) and you assign 250 of the participants to the experimental group (the group that receives the treatment or independent variable).

Why do researchers utilize random selection? The purpose is to increase the generalizability of the results.

By drawing a random sample from a larger population, the goal is that the sample will be representative of the larger group and less likely to be subject to bias.

Factors Involved

Imagine a researcher is selecting people to participate in a study. To pick participants, they may choose people using a technique that is the statistical equivalent of a coin toss.

They may begin by using random selection to pick geographic regions from which to draw participants. They may then use the same selection process to pick cities, neighborhoods, households, age ranges, and individual participants.

Another important thing to remember is that larger sample sizes tend to be more representative. Even random selection can lead to a biased or limited sample if the sample size is small.

When the sample size is small, an unusual participant can have an undue influence over the sample as a whole. Using a larger sample size tends to dilute the effects of unusual participants and prevent them from skewing the results.

Lin L.  Bias caused by sampling error in meta-analysis with small sample sizes .  PLoS ONE . 2018;13(9):e0204056. doi:10.1371/journal.pone.0204056

Elmes DG, Kantowitz BH, Roediger HL.  Research Methods in Psychology. Belmont, CA: Wadsworth; 2012.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

• Math Article

Random Sampling

In statistics, sampling is a method of selecting the subset of the population to make statistical inferences. From the sample, the characteristics of the whole population can be estimated. Sampling in market research can be classified into two different types, namely probability sampling and non-probability sampling. In this article, we are going to discuss one of the types of probability sampling called “Random Sampling” in detail with its definition, different types of random sampling, formulas and examples.

Table of Contents:

• Random sampling Definition
• Simple Random Sampling
• Systematic Sampling
• Stratified Sampling

Clustered Sampling

Random sampling formula, random sampling definition.

Random sampling is a method of choosing a sample of observations from a population to make assumptions about the population. It is also called probability sampling . The counterpart of this sampling is Non-probability sampling or Non-random sampling. The primary types of this sampling are simple random sampling, stratified sampling, cluster sampling, and multistage sampling. In the sampling methods , samples which are not arbitrary are typically called convenience samples.

The primary feature of probability sampling is that the choice of observations must occur in a ‘random’ way such that they do not differ in any significant way from observations, which are not sampled. We assume here that statistical experiments contain data that is gathered through random sampling.

Type of Random Sampling

The random sampling method uses some manner of a random choice. In this method, all the suitable individuals have the possibility of choosing the sample from the whole sample space. It is a time consuming and expensive method. The advantage of using probability sampling is that it ensures the sample that should represent the population. There are four major types of this sampling method, they are;

Now let us discuss its types one by one here.

Simple random sampling

In this sampling method, each item in the population has an equal and likely possibility of getting selected in the sample (for example, each member in a group is marked with a specific number). Since the selection of item completely depends on the possibility, therefore this method is called “ Method of chance Selection” . Also, the sample size is large, and the item is selected randomly. Thus it is known as “ Representative Sampling” .

Systematic Random Sampling

In this method, the items are chosen from the destination population by choosing the random selecting point and picking the other methods after a fixed sample period. It is equal to the ratio of the total population size and the required population size.

Stratified Random Sampling

In this sampling method, a population is divided into subgroups to obtain a simple random sample from each group and complete the sampling process (for example, number of girls in a class of 50 strength). These small groups are called strata . The small group is created based on a few features in the population. After dividing the population into smaller groups, the researcher randomly selects the sample.

Cluster sampling is similar to stratified sampling, besides the population is divided into a large number of subgroups (for example, hundreds of thousands of strata or subgroups). After that, some of these subgroups are chosen at random and simple random samples are then gathered within these subgroups. These subgroups are known as clusters . It is basically utilised to lessen the cost of data compilation.

If P is the probability, n is the sample size, and N is the population. Then;

• The chance of getting a sample selected only once is given by;

P = 1 – (N-1/N).(N-2/N-1)…..(N-n/N-(n-1))

Cancelling = 1-(N-n/n)

• The chance of getting a sample selected more than once is given by;

P = 1-(1-(1/N)) n

Advantages of Simple Random Sampling

Some of the advantages of random sampling are as follows:

• It helps to reduce the bias involved in the sample, compared to other methods of sampling and it is considered as a fair method of sampling.
• This method does not require any technical knowledge, as it is a fundamental method of collecting the data.
• The data collected through this method is well informed. 
• As the population size is large in the simple random sampling method, researchers can create the sample size that they want.
• It is easy to pick the smaller sample size from the existing larger population.

Random Sampling Example

Suppose a firm has 1000 employees in which 100 of them have to be selected for onsite work. All their names will be put in a basket to pull 100 names out of those. Now, each employee has an equal chance of getting selected, so we can also easily calculate the probability ( P ) of a given employee being selected since we know the sample size ( n ) and the population size( N ).

Therefore, the chance of selection of an employee only once is;

P = n/N = 100/1000 = 10%

And the chance of selection of an employee more than once is;

P = 1 – (999/1000) 100

Frequently Asked Questions on Random Sampling

What is meant by random sampling.

The random sampling method is the sampling method, in which each item in the population has an equal chance of being selected in the sample. Hence, this method is also called the method of chance sampling.

Is a simple random sampling method a probability Sampling?

Yes, the simple random sampling method is one of the types of probability sampling.

Mention two advantages of simple random sampling?

The simple random sampling method does not require any technical knowledge. Compared to the other sampling methods, the simple random sampling method reduces the bias involved in the sample.

What are the different methods of probability sampling?

The different methods of probability sampling are: Simple random sampling Systematic sampling Clustered sampling Stratified random sampling

Which sampling method is called the method of chance?

The simple random sampling method is also called the method of chance, as the selection of items completely depends on luck or probability.

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What Is a Simple Random Sample?

• How It Works
• Conducting a Simple Random Sample

Random Sampling Techniques

• Simple Random vs. Other Methods
• Pros and Cons
• Simple Random Sample FAQs

The Bottom Line

• Corporate Finance
• Financial Analysis

Simple Random Sampling: 6 Basic Steps With Examples

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A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. A simple random sample is meant to be an unbiased representation of a group.

Key Takeaways

• A simple random sample takes a small, random portion of the entire population to represent the entire data set, where each member has an equal probability of being chosen.
• Researchers can create a simple random sample using methods like lotteries or random draws.
• A sampling error can occur with a simple random sample if the sample does not end up accurately reflecting the population it is supposed to represent.
• Simple random samples are determined by assigning sequential values to each item within a population, then randomly selecting those values.
• Simple random sampling provides a different sampling approach compared to systematic sampling, stratified sampling, or cluster sampling.

Investopedia / Madelyn Goodnight

Understanding a Simple Random Sample

Researchers can create a simple random sample using a couple of methods. With a lottery method, each member of the population is assigned a number, after which numbers are selected at random.

An example of a simple random sample would be the names of 25 employees being chosen out of a hat from a company of 250 employees. In this case, the population is all 250 employees, and the sample is random because each employee has an equal chance of being chosen. Random sampling is used in science to conduct randomized control tests or for blinded experiments.

The example in which the names of 25 employees out of 250 are chosen out of a hat is an example of the lottery method at work. Each of the 250 employees would be assigned a number between 1 and 250, after which 25 of those numbers would be chosen at random.

Because individuals who make up the subset of the larger group are chosen at random, each individual in the large population set has the same probability of being selected. This creates, in most cases, a balanced subset that carries the greatest potential for representing the larger group as a whole.

For larger populations, a manual lottery method can be quite onerous. Selecting a random sample from a large population usually requires a computer-generated process, by which the same methodology as the lottery method is used, only the number assignments and subsequent selections are performed by computers, not humans.

Room for Error

With a simple random sample, there has to be room for error represented by a plus and minus variance ( sampling error ). For example, if in a high school of 1,000 students a survey were to be taken to determine how many students are left-handed, random sampling can determine that eight out of the 100 sampled are left-handed. The conclusion would be that 8% of the student population of the high school are left-handed, when in fact the global average would be closer to 10%.

The same is true regardless of the subject matter. A survey on the percentage of the student population that has green eyes or is physical disability would result in a mathematical probability based on a simple random survey, but always with a plus or minus variance. The only way to have a 100% accuracy rate would be to survey all 1,000 students which, while possible, would be impractical.

Although simple random sampling is intended to be an unbiased approach to surveying, sample selection bias can occur. When a sample set of the larger population is not inclusive enough, representation of the full population is skewed and requires additional sampling techniques.

How to Conduct a Simple Random Sample

The simple random sampling process entails size steps. Each step much be performed in sequential order.

Step 1: Define the Population

The origin of statistical analysis is to determine the population base. This is the group in which you wish to learn more about, confirm a hypothesis , or determine a statistical outcome. This step is to simply identify what that population base is and to ensure that group will adequately cover the outcome you are trying to solve for.

Example: I wish to learn how the stocks of the largest companies in the United States have performed over the past 20 years. My population is the largest companies in the United States as determined by the S&P 500.

Step 2: Choose Sample Size

Before picking the units within a population, we need to determine how many units to select This sample size may be constrained based on the amount of time, capital rationing , or other resources available to analyze the sample. However, be mindful to pick a sample size large enough to be truly representative of the population. In the example above, there are constrains in analyzing the performance for every stock in the S&P 500, so we only want to analyze a sub-set of this population.

Example: My sample size will be 20 companies from the S&P 500.

Step 3: Determine Population Units

In our example, the items within the population are easy to determine as they've already been identified for us (i.e. the companies listed within the S&P 500). However, imagine analyzing the students currently enrolled at a university or food products being sold at a grocery store. This steps entails crafting the entire list of all items within your population.

Example: Using exchange information, I copy the companies comprising the S&P 500 into an Excel spreadsheet.

Step 4: Assign Numerical Values

The simple random sample process call for every unit within the population receiving an unrelated numerical value. This is often assigned based on how the data may be filtered. For example, I could assign the numbers 1 to 500 to the companies based on market cap , alphabetical, or company formation date. How the values are assigned doesn't entirely matter; all that matters is each value is sequential and each value has an equal chance of being selected.

Example: I assign the numbers 1 through 500 to the companies in the S&P 500 based on alphabetical order of the current CEO, with the first company receiving the value '1' and the last company receiving the value '500'.

Step 5: Select Random Values

In step 2, we selected the number of items we wanted to analyze within our population. For the running example, we choose to analyze 20 items. In the fifth step, we randomly select 20 numbers of the values assigned to our variables. In the running example, this is the numbers 1 through 500. There are multiple ways to randomly select these 20 numbers discussed later in this article.

Example: Using the random number table, I select the numbers 2, 7, 17, 67, 68, 75, 77, 87, 92, 101, 145, 201, 222, 232, 311, 333, 376, 401, 478, and 489.

Step 6: Identify Sample

The last step of a simple random sample is the bridge step 4 and step 5. Each of the random variables selected in the prior step corresponds to a item within our population. The sample is selected by identifying which random values were chosen and which population items those values match.

Example: My sample consists of the 2nd item in the list of companies alphabetically listed by CEO's last name. My sample also consists of company number 7, 17, 67, etc.

There is no single method for determining the random values to be selected (i.e. Step 5 above). The analyst can not simply choose numbers at random as there may not be randomness with numbers. For example, the analyst's wedding anniversary may be the 24th, so they may consciously (or subconsciously) pick the random value 24. Instead, the analyst may choose one of the following methods:

• Random lottery. Whether by ping-pong ball or slips of paper, each population number receives an equivalent item that is stored in a box or other indistinguishable container. Then, random numbers are selected by pulling or selecting items without view from the container.
• Physical Methods. Simple, early methods of random selection may use dice, flipping coins, or spinning wheels. Each outcome is assigned a value or outcome relating to the population.
• Random number table. Many statistics and research books contain sample tables with randomized numbers.
• Online random number generator. Many online tools exist where the analyst inputs the population size and sample size to be selected.
• Random numbers from Excel . Numbers can be selected in Excel using the =RANDBETWEEN formula. A cell containing =RANDBETWEEN(1,5) will selected a single random number between 1 and 5.

When pulling together a sample, consider getting assistance from a colleague or independent person. They may be able to identify biases or discrepancies you may not be aware of.

Simple Random vs. Other Sampling Methods

Simple random vs. stratified random sample.

A simple random sample is used to represent the entire data population. A stratified random sample divides the population into smaller groups, or strata, based on shared characteristics.

Unlike simple random samples, stratified random samples are used with populations that can be easily broken into different subgroups or subsets. These groups are based on certain criteria, then elements from each are randomly chosen in proportion to the group's size versus the population. In our example above, S&P 500 companies could have broken into headquarter geographical region or industry.

This method of sampling means there will be selections from each different group—the size of which is based on its proportion to the entire population. Researchers must ensure the strata do not overlap. Each point in the population must only belong to one stratum so each point is  mutually exclusive . Overlapping strata would increase the likelihood that some data are included, thus skewing the sample.

Simple Random vs. Systematic Sampling

Systematic sampling entails selecting a single random variable, and that variable determines the internal in which the population items are selected. For example, if the number 37 was chosen, the 37th company on the list sorted by CEO last name would be selected by the sample. Then, the 74th (i.e. the next 37th) and the 111st (i.e. the next 37th after that) would be added as well.

Simple random sampling does not have a starting point; therefore, there is the risk that the population items selected at random may cluster. In our example, there may be an abundance of CEOs with the last name that start with the letter 'F'. Systematic sampling strives to even further reduce bias to ensure these clusters do not happen.

Simple Random vs. Cluster Sampling

Cluster sampling can occur as a one-stage cluster or two-stage cluster. In a one-stage cluster, items within a population are put into comparable groupings; using our example, companies are grouped by year formed. Then, sampling occurs within these clusters.

Two-stage cluster sampling occurs when clusters are formed through random selection. The population is not clustered with other similar items. Then, sample items are randomly selected within each cluster.

Simple random sampling does not cluster any population sets. Though sample random sampling may be a simpler, clustering (especially two-stage clustering) may enhance the randomness of sample items. In addition, cluster sampling may provide a deeper analysis on a specific snapshot of a population which may or may not enhance the analysis.

Advantages and Disadvantages of Simple Random Samples

While simple random samples are easy to use, they do come with key disadvantages that can render the data useless.

Advantages of Simple Random Sample

Ease of use represents the biggest advantage of simple random sampling. Unlike more complicated sampling methods, such as stratified random sampling and probability sampling, no need exists to divide the population into sub-populations or take any other additional steps before selecting members of the population at random.

A simple random sample is meant to be an unbiased representation of a group. It is considered a fair way to select a sample from a larger population since every member of the population has an equal chance of getting selected. Therefore, simple random sampling is known for its randomness and less chance of sampling bias.

Disadvantages of Simple Random Sample

A sampling error can occur with a simple random sample if the sample does not end up accurately reflecting the population it is supposed to represent. For example, in our simple random sample of 25 employees, it would be possible to draw 25 men even if the population consisted of 125 women, 125 men, and 125 nonbinary people.

For this reason, simple random sampling is more commonly used when the researcher knows little about the population. If the researcher knew more, it would be better to use a different sampling technique, such as stratified random sampling, which helps to account for the differences within the population, such as age, race, or gender.

Other disadvantages include the fact that for sampling from large populations, the process can be time-consuming and costly compared to other methods. Researchers may find a certain project not worth the endeavor of its cost-benefit analysis does not generate positive results. As every unit has to be assigned an identifying or sequential number prior to the selection process, this task may be difficult based on the method of data collection or size of the data set.

Simple Random Sampling

Each item within a population has an equal chance of being selected

There is less of a chance of sampling bias as every item is randomly selected

This sampling method is easy and convenient for data sets already listed or digitally stored

Incomplete population demographics may exclude certain groups from being sampled

Random selection means the sample may not be truly representative of the population

Depending on the data set size and format, random sampling may be a time-intensive process

Why Is a Simple Random Sample Simple?

No easier method exists to extract a research sample from a larger population than simple random sampling. Selecting enough subjects completely at random from the larger population also yields a sample that can be representative of the group being studied.

What Are Some Drawbacks of a Simple Random Sample?

Among the disadvantages of this technique are difficulty gaining access to respondents that can be drawn from the larger population, greater time, greater costs, and the fact that bias can still occur under certain circumstances.

What Is a Stratified Random Sample?

A stratified random sample, in contrast to a simple draw, first divides the population into smaller groups, or strata, based on shared characteristics. Therefore, a stratified sampling strategy will ensure that members from each subgroup are included in the data analysis. Stratified sampling is used to highlight differences between groups in a population, as opposed to simple random sampling, which treats all members of a population as equal, with an equal likelihood of being sampled.

How Are Random Samples Used?

Using simple random sampling allows researchers to make generalizations about a specific population and leave out any bias. Using statistical techniques, inferences and predictions can be made about the population without having to survey or collect data from every individual in that population.

When analyzing a population, simple random sampling is a technique that results in every item within the population to have the same probability of being selected for the sample size. This more basic form of sampling can be expanded upon to derive more complicated sampling methods. However, the process of making a list of all items in a population, assigning each a sequential number, choosing the sample size, and randomly selecting items is a more basic form of selecting units for analysis.

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