how do random sampling and random assignment differ

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Random Sampling vs Random Assignment

Random sampling and Random assignment are two important distinctions, and understanding the difference between the two is important to get accurate and dependable results.

Random sampling is a proper procedure for selecting a subset of bodies from a larger set of bodies, each of which has the same likelihood of being selected. In contrast, Random allocation of participants involves assigning participants to different groups or conditions of the experiment, and this minimizes pre-existing confounding factors.

Table of Content

What is Random Sampling?

What is random assignment, differences between random sampling and random assignment, examples of random sampling and random assignment, applications of random sampling and random assignment, advantages of random sampling and random assignment, disadvantages of random sampling and random assignment, importance of random sampling and random assignment.

Random sampling is a technique in which a smaller number of individuals are picked up from a large number of people within the population in an impartial manner so that no one person within the population has a greater possibility of being selected than any other person.

This technique makes it possible not to have a selection bias, and, therefore, the sample is so constituted that the results can be generalized to the entire population.

Different techniques of random sampling include – Simple random sampling, stratified sampling, and systematic sampling, all of which have different approaches towards achieving the principle of sampling referred to as representativeness.

Random assignment is the process of distributing participants in experimental research in different groups or under different conditions.

This process also guarantees that no participant tends to be placed in a particular group, thus reducing the possibility of selection bias within a given study. In doing so, random assignment enhances the chances of the two groups’ equality at the different stages of an experiment, so the researcher can effectively link results to the treatment or intervention under consideration without worrying about other factors.

This increases the internal reliability of the study and assists in establishing a cause-and-effect relationship.

Differences between Random Sampling and Random Assignment can be learnt using the table added below:

Aspect	Random Sampling	Random Assignment
Purpose	To obtain a representative sample of a larger population.	To evenly distribute participants across different experimental conditions.
Application	Used in surveys and observational studies to ensure sample representativeness.	Used in experiments to control for variables and ensure groups are comparable.
Process	Randomly selects individuals from the population.	Randomly assigns individuals to different groups or conditions.
Outcome	Provides a sample that mirrors the population’s characteristics.	Ensures that differences observed between groups are due to the treatment or intervention.
Focus	Accuracy of the sample in reflecting the population.	Validity of the experiment by controlling for confounding variables.

Various examples of Random Sampling and Random Assignment

Random Sampling	Random Assignment
Surveying 1,000 randomly selected voters to gauge public opinion.	Randomly assigning participants to a treatment or control group in a clinical trial.
Selecting a random sample of students from a school to study academic performance.	Randomly assigning students to either a new teaching method or traditional method group.
Using random sampling to choose households for a national health survey.	Randomly assigning patients to different drug dosage levels in a medical study.
Sampling customers from different regions to assess brand satisfaction.	Randomly assigning participants to different marketing strategies in an advertising experiment.
Drawing a random sample of participants from a population for a psychological study.	Randomly assigning individuals to different therapy types in a behavioral study.

Some applications of Random Sampling and Random Assignment are added in the table below:

Application	Random Sampling	Random Assignment
Public Opinion Polls	Selecting a representative sample of voters to gauge public opinion.	Not applicable; polls use sampling, not assignment.
Clinical Trials	Sampling patients from a larger population for study inclusion.	Randomly assigning participants to treatment or control groups.
Educational Research	Sampling students from different schools to study educational outcomes.	Randomly assigning students to different teaching methods.
Marketing Research	Sampling customers to gather feedback on a product or service.	Randomly assigning customers to different marketing strategies.
Behavioral Studies	Sampling participants from a population to study behavior patterns.	Randomly assigning participants to various experimental conditions.

Some advantages of Random Sampling and Random Assignment are added in the table below:

Advantages	Random Sampling	Random Assignment
Reduces Bias	Minimizes selection bias, ensuring a representative sample.	Balances pre-existing differences between groups, reducing bias.
Generalizability	Ensures findings can be generalized to the larger population.	Enhances internal validity by controlling for confounding variables.
Reliability	Provides a basis for statistical analysis and valid conclusions.	Allows for clear attribution of effects to the treatment or intervention.
Equal Chance	Each member of the population has an equal chance of being selected.	Each participant has an equal chance of being assigned to any group.
Reduces Sampling Error	Helps reduce sampling error by accurately representing the population.	Ensures that any differences observed are due to the experimental conditions.

Some disadvantages of Random Sampling and Random Assignment are added in the table below:

Disadvantages	Random Sampling	Random Assignment
Cost and Time	Can be costly and time-consuming to implement, especially with large populations.	May be logistically challenging and resource-intensive.
Practical Challenges	May face difficulties in achieving a truly random sample due to accessibility issues.	May not always be feasible or ethical, especially in certain contexts.
Representativeness	Small sample sizes may not fully represent the population, affecting accuracy.	Random assignment may not eliminate all sources of bias or variability.
Implementation Issues	Practical difficulties in ensuring true randomness.	Potential for unequal distribution of key variables if sample sizes are small.
Ethical Concerns	May face ethical issues if certain groups are underrepresented.	Ethical dilemmas may arise if one group receives less beneficial treatment.

Importance of Random Sampling and Random Assignment are added in the table below:

Importance	Random Sampling	Random Assignment
Purpose	Ensures the sample represents the population	Ensures participants are evenly distributed across experimental groups.
Bias Reduction	Reduces selection bias in sample selection.	Minimizes pre-existing differences between groups.
Generalizability	Allows findings to be generalized to the population.	Improves the validity of conclusions about the treatment effect.
Validity	Ensures that sample findings reflect the broader population.	Ensures observed effects are due to the intervention, not confounding variables.
Statistical Analysis	Provides a basis for accurate statistical inferences.	Facilitates robust comparison between experimental conditions.

Random sampling and random assignment are two significant techniques in research that act differently yet are equally important in study procedures.

Random sampling makes sure that a sample is selected from the population in a way that will reflect on the whole population, and this helps in reducing bias.
Random assignment , on the other hand, is useful in experimental investigations and aims at assigning the participants to the groups equally since it helps in preventing the influence of external variables and keeps only the treatment or intervention factor active.

Combined, these methods increase the credibility of results, allowing the development of more accurate conclusions based on research. By comprehending each class’s roles, research workers keep their studies and conclusions a lot more precise.

Random SamplingMethod Simple Random Sampling Systematic Sampling vs Random Sampling

FAQs on Random Sampling and Random Assignment

What is the difference between random sampling and random assignment.

Random sampling is the one in which subjects are chosen haphazardly from a population so that every member of that population has the same likelihood of being selected. Random assignment is the process of assigning the participants of an experiment to various groups or conditions in a random manner so that any background difference is not a factor.

What is random sampling, and why is it significant to research?

On the other hand, random sampling helps in achieving a representative sample, which helps in making generalizations and cuts down on selection bias.

Why does random assignment help increase the validity of an experiment?

Random assignment equalizes the variability between groups. This way, any variations that are noticed in the study are attributed to the treatment or the intervention.

What are the types of random sampling that are widely used in research studies?

Some of them are simple random sampling, stratified sampling, and systematic sampling, all of which have different ways of obtaining a representative sample.

Can random assignment be used in all types of research?

Although random assignment is optimum for making experiments with the view of finding cause-and-effect relationships, it may not be possible or even immoral in some cases, like in observational research or some healthcare conditions.

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Random Assignment in Psychology: Definition & Examples

Julia Simkus

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BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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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.

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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:


	Computer programs to generate numbers from the list of participants
	Place the numbers in a container and draw them randomly for each group
	If you have two sets or groups only, you can toss a coin to determine which one will be the regulated or trial group
	If you have three groups, you can roll a dice to determine which participant joins each group.

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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Understanding Random Sampling: Essential Techniques in Data Analysis

Random sampling in statistics is a technique for selecting a subset of individuals from a larger population where each individual has an equal chance of being chosen. This method ensures representative samples, minimizes bias and allows for reliable inferences about the population based on the sample data.

Definition and Importance of Random Sampling

Random sampling is fundamental in data analysis, statistics, and broader scientific research. It refers to the technique of selecting individuals or elements from a population such that each individual has an equal probability of being chosen. This method is essential as it ensures a representative sample, thereby eliminating bias and enabling researchers to draw valid conclusions about the whole population based on the sample data.

The importance of random sampling in data analysis cannot be overstated. Instead, it forms the basis of hypothesis testing, inferential statistics, and prediction modeling. Without random sampling, we risk introducing selection bias into our study, which can lead to inaccurate conclusions and misleading results. The strength of random sampling lies in its ability to mirror the characteristics of the whole population within the sample, enhancing the reliability and validity of the analysis.

In random sampling, every member of a population has an equal chance to be chosen as part of the sample.
It forms the basis of hypothesis testing, inferential statistics, and prediction modeling.
Simple random sampling, the most basic form, is adequate when the population is homogeneous.
Stratified random sampling divides the population into subgroups, ensuring sufficient representation.
Systematic random sampling selects individuals at regular intervals from the population.

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Types of Random Sampling

Simple random sampling.

Simple Random Sampling is the most basic type of random sampling. Each population element has an equal chance of being selected in this method. The selection is often made through a random process, such as using a random number generator or drawing names from a hat. This method is most effective when the population is homogeneous, i.e., when the characteristics of individuals don’t significantly vary. Imagine a small town that wants to survey residents’ satisfaction with local services. They could use simple random sampling by assigning each resident a number and then using a random number generator to select 100 residents to participate in the survey.

Stratified Random Sampling

Stratified Random Sampling is a technique used when the population is not homogeneous. The population is categorized into strata (or subgroups) based on specific characteristics such as age, gender, or geographic location. Then, random sampling is applied within each stratum to select the individuals. This method ensures that each subgroup is adequately represented in the sample. Suppose a national clothing retailer wants to understand customer satisfaction across different age groups. They could divide their customer base into distinct age groups, such as 18-29, 30-39, 40-49, etc., and then perform simple random sampling within these strata to ensure that all age groups are adequately represented.

Systematic Random Sampling

Systematic Random Sampling involves selecting individuals at regular intervals from the population. The first individual is chosen randomly, and then every nth is selected. This method is often used when a complete list of the population is available, and it’s important to note that it requires the assumption that the list is not patterned in any way. Suppose a university wants to assess the effectiveness of its new online learning platform. They could use systematic random sampling by alphabetizing all students and selecting every 10th student for a survey. This method would provide a sample spread evenly across the entire student population.

Cluster Random Sampling

Cluster Random Sampling involves dividing the population into separate groups or clusters, usually based on geographic location. A random sample of clusters is selected, and all individuals within these chosen clusters are included. This method is often used when conducting simple or stratified sampling is costly or impractical. Consider a situation where a government health agency wants to study lifestyle habits nationwide. It would be impractical and expensive to randomly sample individuals from the entire country. Instead, they could use cluster sampling. They might divide the country into clusters by postcode and then randomly select a few postcodes. Every resident within the selected postcodes would be included in the study.

Challenges and Misconceptions about Random Sampling

Despite the importance of random sampling, several challenges and misconceptions can hamper its effective implementation.

One common misconception is that random sampling produces a sample that perfectly represents the population. While random sampling is designed to minimize bias and increase the likelihood of representativeness, it does not guarantee it. There’s always a chance that the sample might not accurately reflect the population due to random variation.

Another challenge is the practical implementation of random sampling. Often, having a complete population list or randomly selecting individuals may be impossible. For instance, respondents self-select to participate in online surveys, which may introduce bias.

Furthermore, there is a typical misconception that a larger sample is always better. While it’s true that increasing the sample size can often decrease the margin of error and increase the confidence level, it also increases the time and cost of data collection and analysis. Therefore, balancing the need for precision with practical considerations is crucial.

In summary, while random sampling is a cornerstone of statistical and data analysis, it has challenges and misconceptions. Understanding these can help researchers and analysts better design and implement their studies for robust, reliable, and meaningful results.

Frequently Asked Questions (FAQs)

The four main types of random sampling are Simple, Stratified, Cluster, and Systematic Random Sampling. Each has its unique application depending on the nature of the population and the research question.

Random sampling is used to pick a representative sample from a larger population, ensuring each individual has an equal chance of being chosen. This minimizes selection bias, making inferences about the population more accurate.

A random sample in statistics is a subset of individuals or data points selected from a larger population. Each individual or point has an equal probability of being chosen.

Random sampling is done by assigning each individual in the population a unique identifier and then using a random process (like a random number generator) to select a subset of individuals.

The “best” random sampling method depends on the specifics of the study, including the nature of the population, the research question, and practical considerations. Each method has its strengths and weaknesses.

The choice of sampling method depends on several factors, including the research question, the nature of the population, the availability of a complete list of the population, and practical constraints such as time and cost.

Challenges of random sampling include practical implementation issues, the potential for nonresponse bias, and the misconception that a larger sample is always better or more representative.

While random sampling can help reduce selection bias, it does not stop all types of bias. For example, it can’t correct measurement errors or biases in data collection.

Stratified random sampling is distinct from simple random sampling. It first divides the population into different subgroups, or strata, based on specific characteristics. Then, simple random sampling is performed within each subset. This ensures that each subgroup is adequately represented in the sample, which can be especially useful when the population is heterogeneous.

Cluster random sampling involves dividing the population into clusters and then randomly selecting a few clusters for study. For instance, a researcher studying educational practices might divide a country into clusters by school districts, then randomly select a few districts. All schools within these selected districts would be included in the study.

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Random Assignment in Experiments

By Jim Frost 4 Comments

Random assignment uses chance to assign subjects to the control and treatment groups in an experiment. This process helps ensure that the groups are equivalent at the beginning of the study, which makes it safer to assume the treatments caused any differences between groups that the experimenters observe at the end of the study.

photogram of tumbling dice to illustrate a process for random assignment.

Huh? That might be a big surprise! At this point, you might be wondering about all of those studies that use statistics to assess the effects of different treatments. There’s a critical separation between significance and causality:

Statistical procedures determine whether an effect is significant.
Experimental designs determine how confidently you can assume that a treatment causes the effect.

In this post, learn how using random assignment in experiments can help you identify causal relationships.

Correlation, Causation, and Confounding Variables

Random assignment helps you separate causation from correlation and rule out confounding variables. As a critical component of the scientific method , experiments typically set up contrasts between a control group and one or more treatment groups. The idea is to determine whether the effect, which is the difference between a treatment group and the control group, is statistically significant. If the effect is significant, group assignment correlates with different outcomes.

However, as you have no doubt heard, correlation does not necessarily imply causation. In other words, the experimental groups can have different mean outcomes, but the treatment might not be causing those differences even though the differences are statistically significant.

The difficulty in definitively stating that a treatment caused the difference is due to potential confounding variables or confounders. Confounders are alternative explanations for differences between the experimental groups. Confounding variables correlate with both the experimental groups and the outcome variable. In this situation, confounding variables can be the actual cause for the outcome differences rather than the treatments themselves. As you’ll see, if an experiment does not account for confounding variables, they can bias the results and make them untrustworthy.

Related posts : Understanding Correlation in Statistics , Causation versus Correlation , and Hill’s Criteria for Causation .

Example of Confounding in an Experiment

A photograph of vitamin capsules to represent our experiment.

Control group: Does not consume vitamin supplements
Treatment group: Regularly consumes vitamin supplements.

Imagine we measure a specific health outcome. After the experiment is complete, we perform a 2-sample t-test to determine whether the mean outcomes for these two groups are different. Assume the test results indicate that the mean health outcome in the treatment group is significantly better than the control group.

Why can’t we assume that the vitamins improved the health outcomes? After all, only the treatment group took the vitamins.

Related post : Confounding Variables in Regression Analysis

Alternative Explanations for Differences in Outcomes

The answer to that question depends on how we assigned the subjects to the experimental groups. If we let the subjects decide which group to join based on their existing vitamin habits, it opens the door to confounding variables. It’s reasonable to assume that people who take vitamins regularly also tend to have other healthy habits. These habits are confounders because they correlate with both vitamin consumption (experimental group) and the health outcome measure.

Random assignment prevents this self sorting of participants and reduces the likelihood that the groups start with systematic differences.

In fact, studies have found that supplement users are more physically active, have healthier diets, have lower blood pressure, and so on compared to those who don’t take supplements. If subjects who already take vitamins regularly join the treatment group voluntarily, they bring these healthy habits disproportionately to the treatment group. Consequently, these habits will be much more prevalent in the treatment group than the control group.

The healthy habits are the confounding variables—the potential alternative explanations for the difference in our study’s health outcome. It’s entirely possible that these systematic differences between groups at the start of the study might cause the difference in the health outcome at the end of the study—and not the vitamin consumption itself!

If our experiment doesn’t account for these confounding variables, we can’t trust the results. While we obtained statistically significant results with the 2-sample t-test for health outcomes, we don’t know for sure whether the vitamins, the systematic difference in habits, or some combination of the two caused the improvements.

Learn why many randomized clinical experiments use a placebo to control for the Placebo Effect .

Experiments Must Account for Confounding Variables

Your experimental design must account for confounding variables to avoid their problems. Scientific studies commonly use the following methods to handle confounders:

Use control variables to keep them constant throughout an experiment.
Statistically control for them in an observational study.
Use random assignment to reduce the likelihood that systematic differences exist between experimental groups when the study begins.

Let’s take a look at how random assignment works in an experimental design.

Random Assignment Can Reduce the Impact of Confounding Variables

Note that random assignment is different than random sampling. Random sampling is a process for obtaining a sample that accurately represents a population .

Photo of a coin toss to represent how we can incorporate random assignment in our experiment.

Random assignment uses a chance process to assign subjects to experimental groups. Using random assignment requires that the experimenters can control the group assignment for all study subjects. For our study, we must be able to assign our participants to either the control group or the supplement group. Clearly, if we don’t have the ability to assign subjects to the groups, we can’t use random assignment!

Additionally, the process must have an equal probability of assigning a subject to any of the groups. For example, in our vitamin supplement study, we can use a coin toss to assign each subject to either the control group or supplement group. For more complex experimental designs, we can use a random number generator or even draw names out of a hat.

Random Assignment Distributes Confounders Equally

The random assignment process distributes confounding properties amongst your experimental groups equally. In other words, randomness helps eliminate systematic differences between groups. For our study, flipping the coin tends to equalize the distribution of subjects with healthier habits between the control and treatment group. Consequently, these two groups should start roughly equal for all confounding variables, including healthy habits!

Random assignment is a simple, elegant solution to a complex problem. For any given study area, there can be a long list of confounding variables that you could worry about. However, using random assignment, you don’t need to know what they are, how to detect them, or even measure them. Instead, use random assignment to equalize them across your experimental groups so they’re not a problem.

Because random assignment helps ensure that the groups are comparable when the experiment begins, you can be more confident that the treatments caused the post-study differences. Random assignment helps increase the internal validity of your study.

Comparing the Vitamin Study With and Without Random Assignment

Let’s compare two scenarios involving our hypothetical vitamin study. We’ll assume that the study obtains statistically significant results in both cases.

Scenario 1: We don’t use random assignment and, unbeknownst to us, subjects with healthier habits disproportionately end up in the supplement treatment group. The experimental groups differ by both healthy habits and vitamin consumption. Consequently, we can’t determine whether it was the habits or vitamins that improved the outcomes.

Scenario 2: We use random assignment and, consequently, the treatment and control groups start with roughly equal levels of healthy habits. The intentional introduction of vitamin supplements in the treatment group is the primary difference between the groups. Consequently, we can more confidently assert that the supplements caused an improvement in health outcomes.

For both scenarios, the statistical results could be identical. However, the methodology behind the second scenario makes a stronger case for a causal relationship between vitamin supplement consumption and health outcomes.

How important is it to use the correct methodology? Well, if the relationship between vitamins and health outcomes is not causal, then consuming vitamins won’t cause your health outcomes to improve regardless of what the study indicates. Instead, it’s probably all the other healthy habits!

Learn more about Randomized Controlled Trials (RCTs) that are the gold standard for identifying causal relationships because they use random assignment.

Drawbacks of Random Assignment

Random assignment helps reduce the chances of systematic differences between the groups at the start of an experiment and, thereby, mitigates the threats of confounding variables and alternative explanations. However, the process does not always equalize all of the confounding variables. Its random nature tends to eliminate systematic differences, but it doesn’t always succeed.

Sometimes random assignment is impossible because the experimenters cannot control the treatment or independent variable. For example, if you want to determine how individuals with and without depression perform on a test, you cannot randomly assign subjects to these groups. The same difficulty occurs when you’re studying differences between genders.

In other cases, there might be ethical issues. For example, in a randomized experiment, the researchers would want to withhold treatment for the control group. However, if the treatments are vaccinations, it might be unethical to withhold the vaccinations.

Other times, random assignment might be possible, but it is very challenging. For example, with vitamin consumption, it’s generally thought that if vitamin supplements cause health improvements, it’s only after very long-term use. It’s hard to enforce random assignment with a strict regimen for usage in one group and non-usage in the other group over the long-run. Or imagine a study about smoking. The researchers would find it difficult to assign subjects to the smoking and non-smoking groups randomly!

Fortunately, if you can’t use random assignment to help reduce the problem of confounding variables, there are different methods available. The other primary approach is to perform an observational study and incorporate the confounders into the statistical model itself. For more information, read my post Observational Studies Explained .

Read About Real Experiments that Used Random Assignment

I’ve written several blog posts about studies that have used random assignment to make causal inferences. Read studies about the following:

Flu Vaccinations
COVID-19 Vaccinations

Sullivan L. Random assignment versus random selection . SAGE Glossary of the Social and Behavioral Sciences, SAGE Publications, Inc.; 2009.

Reader Interactions

November 13, 2019 at 4:59 am

Hi Jim, I have a question of randomly assigning participants to one of two conditions when it is an ongoing study and you are not sure of how many participants there will be. I am using this random assignment tool for factorial experiments. http://methodologymedia.psu.edu/most/rannumgenerator It asks you for the total number of participants but at this point, I am not sure how many there will be. Thanks for any advice you can give me, Floyd

May 28, 2019 at 11:34 am

Jim, can you comment on the validity of using the following approach when we can’t use random assignments. I’m in education, we have an ACT prep course that we offer. We can’t force students to take it and we can’t keep them from taking it either. But we want to know if it’s working. Let’s say that by senior year all students who are going to take the ACT have taken it. Let’s also say that I’m only including students who have taking it twice (so I can show growth between first and second time taking it). What I’ve done to address confounders is to go back to say 8th or 9th grade (prior to anyone taking the ACT or the ACT prep course) and run an analysis showing the two groups are not significantly different to start with. Is this valid? If the ACT prep students were higher achievers in 8th or 9th grade, I could not assume my prep course is effecting greater growth, but if they were not significantly different in 8th or 9th grade, I can assume the significant difference in ACT growth (from first to second testing) is due to the prep course. Yes or no?

May 26, 2019 at 5:37 pm

Nice post! I think the key to understanding scientific research is to understand randomization. And most people don’t get it.

May 27, 2019 at 9:48 pm

Thank you, Anoop!

I think randomness in an experiment is a funny thing. The issue of confounding factors is a serious problem. You might not even know what they are! But, use random assignment and, voila, the problem usually goes away! If you can’t use random assignment, suddenly you have a whole host of issues to worry about, which I’ll be writing about in more detail in my upcoming post about observational experiments!

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Scientific Research and Methodology

7.3 random allocation vs random sampling.

Random sampling and random allocation are two different concepts (Fig. 7.4 ), that serve two different purposes, but are often confused:

Random sampling allows results to be generalised to a larger population, and impacts external validity. It concerns how the sample is found to study.
Random allocation tries to eliminate confounding issues, by evening-out possible confounders across treatment groups. Random allocation of treatments helps establish cause-and-effect, and impacts internal validity. It concerns how the members of the chosen sample get the treatments .

FIGURE 7.4: Comparing random allocation and random sampling

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Simple Random Sampling | Definition, Steps & Examples

Simple Random Sampling | Definition, Steps & Examples

Published on August 28, 2020 by Lauren Thomas . Revised on December 18, 2023.

A simple random sample is a randomly selected subset of a population. In this sampling method, each member of the population has an exactly equal chance of being selected.

This method is the most straightforward of all the probability sampling methods , since it only involves a single random selection and requires little advance knowledge about the population. Because it uses randomization, any research performed on this sample should have high internal and external validity, and be at a lower risk for research biases like sampling bias and selection bias .

When to use simple random sampling, how to perform simple random sampling, other interesting articles, frequently asked questions about simple random sampling.

Simple random sampling is used to make statistical inferences about a population. It helps ensure high internal validity : randomization is the best method to reduce the impact of potential confounding variables .

In addition, with a large enough sample size, a simple random sample has high external validity : it represents the characteristics of the larger population.

However, simple random sampling can be challenging to implement in practice. To use this method, there are some prerequisites:

You have a complete list of every member of the population .
You can contact or access each member of the population if they are selected.
You have the time and resources to collect data from the necessary sample size.

Simple random sampling works best if you have a lot of time and resources to conduct your study, or if you are studying a limited population that can easily be sampled.

In some cases, it might be more appropriate to use a different type of probability sampling:

Systematic sampling involves choosing your sample based on a regular interval, rather than a fully random selection. It can also be used when you don’t have a complete list of the population.
Stratified sampling is appropriate when you want to ensure that specific characteristics are proportionally represented in the sample. You split your population into strata (for example, divided by gender or race), and then randomly select from each of these subgroups.
Cluster sampling is appropriate when you are unable to sample from the entire population. You divide the sample into clusters that approximately reflect the whole population, and then choose your sample from a random selection of these clusters.

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There are 4 key steps to select a simple random sample.

Step 1: Define the population

Start by deciding on the population that you want to study.

It’s important to ensure that you have access to every individual member of the population, so that you can collect data from all those who are selected for the sample.

Step 2: Decide on the sample size

Next, you need to decide how large your sample size will be. Although larger samples provide more statistical certainty, they also cost more and require far more work.

There are several potential ways to decide upon the size of your sample, but one of the simplest involves using a formula with your desired confidence interval and confidence level , estimated size of the population you are working with, and the standard deviation of whatever you want to measure in your population.

The most common confidence interval and levels used are 0.05 and 0.95, respectively. Since you may not know the standard deviation of the population you are studying, you should choose a number high enough to account for a variety of possibilities (such as 0.5).

You can then use a sample size calculator to estimate the necessary sample size.

Step 3: Randomly select your sample

This can be done in one of two ways: the lottery or random number method.

In the lottery method , you choose the sample at random by “drawing from a hat” or by using a computer program that will simulate the same action.

In the random number method , you assign every individual a number. By using a random number generator or random number tables, you then randomly pick a subset of the population. You can also use the random number function (RAND) in Microsoft Excel to generate random numbers.

Step 4: Collect data from your sample

Finally, you should collect data from your sample.

To ensure the validity of your findings, you need to make sure every individual selected actually participates in your study. If some drop out or do not participate for reasons associated with the question that you’re studying, this could bias your findings.

For example, if young participants are systematically less likely to participate in your study, your findings might not be valid due to the underrepresentation of this group.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

Implicit bias
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic
Social desirability bias

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

The American Community Survey is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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The Random Selection Experiment Method

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."

Simple Random Sampling

Definition and Different Approaches

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Recommended Reading
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Simple random sampling is the most basic and common type of sampling method used in quantitative social science research and in scientific research generally . The main benefit of the simple random sample is that each member of the population has an equal chance of being chosen for the study. This means that it guarantees that the sample chosen is representative of the population and that the sample is selected in an unbiased way. In turn, the statistical conclusions drawn from the analysis of the sample will be valid .

There are multiple ways of creating a simple random sample. These include the lottery method, using a random number table, using a computer, and sampling with or without replacement.

Lottery Method of Sampling

The lottery method of creating a simple random sample is exactly what it sounds like. A researcher randomly picks numbers, with each number corresponding to a subject or item, in order to create the sample. To create a sample this way, the researcher must ensure that the numbers are well mixed before selecting the sample population.

Using a Random Number Table

One of the most convenient ways of creating a simple random sample is to use a random number table . These are commonly found at the back of textbooks on the topics of statistics or research methods. Most random number tables will have as many as 10,000 random numbers. These will be composed of integers between zero and nine and arranged in groups of five. These tables are carefully created to ensure that each number is equally probable, so using it is a way to produce a random sample required for valid research outcomes.

To create a simple random sample using a random number table just follow these steps.

Number each member of the population 1 to N.
Determine the population size and sample size.
Select a starting point on the random number table. (The best way to do this is to close your eyes and point randomly onto the page. Whichever number your finger is touching is the number you start with.)
Choose a direction in which to read (up to down, left to right, or right to left).
Select the first n numbers (however many numbers are in your sample) whose last X digits are between 0 and N. For instance, if N is a 3 digit number, then X would be 3. Put another way, if your population contained 350 people, you would use numbers from the table whose last 3 digits were between 0 and 350. If the number on the table was 23957, you would not use it because the last 3 digits (957) is greater than 350. You would skip this number and move to the next one. If the number is 84301, you would use it and you would select the person in the population who is assigned the number 301.
Continue this way through the table until you have selected your entire sample , whatever your n is. The numbers you selected then correspond to the numbers assigned to the members of your population, and those selected become your sample.

Using a Computer

In practice, the lottery method of selecting a random sample can be quite burdensome if done by hand. Typically, the population being studied is large and choosing a random sample by hand would be very time-consuming. Instead, there are several computer programs that can assign numbers and select n random numbers quickly and easily. Many can be found online for free.

Sampling With Replacement

Sampling with replacement is a method of random sampling in which members or items of the population can be chosen more than once for inclusion in the sample. Let’s say we have 100 names each written on a piece of paper. All of those pieces of paper are put into a bowl and mixed up. The researcher picks a name from the bowl, records the information to include that person in the sample, then puts the name back in the bowl, mixes up the names, and selects another piece of paper. The person that was just sampled has the same chance of being selected again. This is known as sampling with replacement.

Sampling Without Replacement

Sampling without replacement is a method of random sampling in which members or items of the population can only be selected one time for inclusion in the sample. Using the same example above, let’s say we put the 100 pieces of paper in a bowl, mix them up, and randomly select one name to include in the sample. This time, however, we record the information to include that person in the sample and then set that piece of paper aside rather than putting it back into the bowl. Here, each element of the population can only be selected one time.

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4 Types of Random Sampling Techniques Explained

Collect unbiased data utilizing these four types of random sampling techniques: systematic, stratified, cluster, and simple random sampling.

Random sampling means choosing a subset of a larger population where each sample has an equal probability of being chosen. Random samples are used in statistical and scientific research to reduce sampling bias and get sample data that is generally representative of a population, which help form unbiased conclusions.

4 Types of Random Sampling Techniques

Simple random sampling.
Stratified random sampling.
Cluster random sampling.
Systematic random sampling.

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What Is Random Sampling?

Random sampling simply describes a state wherein every element in a population has an equal chance of being chosen for the sample. Sounds simple, right? Well, it’s a lot easier said than done because you must consider a lot of logistics in order to minimize bias.

Why Is Random Sampling Important?

If you’re a data scientist and want to develop models, or a researcher who wants to analyze a population, you need data. And if you need data, someone needs to collect that data. And if someone is collecting data, they need to make sure that it isn’t biased or it will be extremely costly in the long run.

Therefore, if you want to collect unbiased data and create more accurate data models , then you need to know about random sampling and how it works.

More on Data Science Importance Sampling Explained

Types of Random Sampling

There are four main types of random sampling techniques: simple random sampling, stratified random sampling, cluster random sampling and systematic random sampling. Each is used for different sampling situations.

1. Simple Random Sampling

Simple random sampling requires the use of randomly generated numbers to choose a sample. More specifically, it initially requires a sampling frame, which is a list or database of all members of a population. You can then randomly generate a number for each element, using Excel for example, and take the first n number of samples that you require.

A chart showing a random sampling technique

To give an example, imagine the table on the right was your sampling frame. Using software like Excel, you can then generate random numbers for each element in the sampling frame. If you need a sample size of three, then you would take the samples with the random numbers from one to three.

2. Stratified Random Sampling

Stratified random sampling involves dividing a population into groups with similar attributes and randomly sampling each group.

A graphic illustrating a random sampling technique

This method ensures that different segments in a population are equally represented. To give an example, imagine a survey is conducted at a school to determine overall satisfaction. Here, stratified random sampling can equally represent the opinions of students in each department.

3. Cluster Random Sampling

Cluster sampling starts by dividing a population into groups or clusters. What makes this different from stratified sampling is that each cluster must be representative of the larger population. Then, you randomly select entire clusters to sample.

A graphic illustrating random sampling techniques

For example, if a school had five different eighth grade classes, cluster random sampling means any one class would serve as a sample.

4. Systematic Random Sampling

Systematic random sampling is a common technique in which you sample every k th element. For example, if you were conducting surveys at a mall, you might survey every 100th person that walks in.

If you have a sampling frame, then you would divide the size of the frame, N , by the desired sample size, n , to get the index number, k . You would then choose every k th element in the frame to create your sample.

Using the same charts from the first example, if we wanted a sample size of two this time, then we would take every third row in the sampling frame.

Frequently Asked Questions

What is random sampling.

Random sampling involves collecting a subset of samples from a population in a way where each sample has an equal chance of being chosen. Random samples are used to ensure a sample adequately represents the larger population and to minimize sampling bias in research results.

What are the 4 types of simple random sampling?

The 4 main types of random sampling are:

Simple random sampling
Stratified random sampling
Cluster random sampling
Systematic random sampling

Which is an example of a random sample?

An example of a random sample would be randomly choosing the names of 10 people from a hat containing the names from a group of 100 people.

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Methodology
Simple Random Sampling | Definition, Steps & Examples

Simple Random Sampling | Definition, Steps & Examples

Published on 3 May 2022 by Lauren Thomas . Revised on 18 December 2023.

A simple random sample is a randomly selected subset of a population . In this sampling method, each member of the population has an exactly equal chance of being selected, minimising the risk of selection bias .

This method is the most straightforward of all the probability sampling methods , since it only involves a single random selection and requires little advance knowledge about the population. Because it uses randomisation, any research performed on this sample should have high internal and external validity.

When to use simple random sampling, how to perform simple random sampling, frequently asked questions about simple random sampling.

Simple random sampling is used to make statistical inferences about a population. It helps ensure high internal validity : randomisation is the best method to reduce the impact of potential confounding variables .

In addition, with a large enough sample size, a simple random sample has high external validity : it represents the characteristics of the larger population.

However, simple random sampling can be challenging to implement in practice. To use this method, there are some prerequisites:

You have a complete list of every member of the population.
You can contact or access each member of the population if they are selected.
You have the time and resources to collect data from the necessary sample size.

Simple random sampling works best if you have a lot of time and resources to conduct your study, or if you are studying a limited population that can easily be sampled.

In some cases, it might be more appropriate to use a different type of probability sampling:

Systematic sampling involves choosing your sample based on a regular interval, rather than a fully random selection. It can also be used when you don’t have a complete list of the population.
Stratified sampling is appropriate when you want to ensure that specific characteristics are proportionally represented in the sample. You split your population into strata (for example, divided by gender or race), and then randomly select from each of these subgroups.
Cluster sampling is appropriate when you are unable to sample from the entire population. You divide the sample into clusters that approximately reflect the whole population, and then choose your sample from a random selection of these clusters.

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There are four key steps to select a simple random sample.

Step 1: Define the population

Start by deciding on the population that you want to study.

It’s important to ensure that you have access to every individual member of the population, so that you can collect data from all those who are selected for the sample.

Step 2: Decide on the sample size

Next, you need to decide how large your sample size will be. Although larger samples provide more statistical certainty, they also cost more and require far more work.

You can then use a sample size calculator to estimate the necessary sample size.

Step 3: Randomly select your sample

This can be done in one of two ways: the lottery or random number method.

In the lottery method , you choose the sample at random by ‘drawing from a hat’ or by using a computer program that will simulate the same action.

Step 4: Collect data from your sample

Finally, you should collect data from your sample.

For example, if young participants are systematically less likely to participate in your study, your findings might not be valid due to the underrepresentation of this group.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data are then collected from as large a percentage as possible of this random subset.

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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Random Sampling vs Random Assignment

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Random Assignment in Psychology: Definition & Examples

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Random Selection vs. Random Assignment

Random Assignment vs Random Sampling

When to Use Random Assignment

How to Use Random Assignment

When is Random Assignment not used?

Drawbacks of Random Assignment

What is the difference between random sampling and random assignment?

Does random assignment increase internal validity?

Does random assignment reduce sampling error?

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Does random assignment eliminate confounding variables?

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

Further Reading

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Random Assignment – A Simple Introduction with Examples

Random Assignment – In a Nutshell

Definition: Random assignment

Importance of random assignment

Random assignment vs. random sampling

How to use random assignment

Random assignment in block experimental designs

When random assignment is not used

Comparing different groups

No ethical justifiability

What is the definition of random assignment?

When is random assignment applicable?

What is the importance of random assignment?

When should you NOT use random assignment

Understanding Random Sampling: Essential Techniques in Data Analysis

Definition and Importance of Random Sampling

Types of Random Sampling

Stratified Random Sampling

Systematic Random Sampling

Cluster Random Sampling

Challenges and Misconceptions about Random Sampling

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Simple Random Sampling | Definition, Steps & Examples

Table of contents

Prevent plagiarism. Run a free check.

Step 1: Define the population

Step 2: Decide on the sample size

Step 3: Randomly select your sample

Step 4: Collect data from your sample

Cite this Scribbr article