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Simple Random Sampling Method: Definition & Example

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Simple random sampling is a technique in which each member of a population has an equal chance of being chosen through an unbiased selection method. Each subject in the sample is given a number, and then the sample is chosen randomly.

The random sampling method is one of the simplest and most common forms of collecting data, as it provides an unbiased representation of a group. The random subset of selected individuals represents an entire data set.

The goal of simple random sampling is to create a manageable, balanced subset of individuals that is representative of a larger group that would otherwise be too challenging to sample.

For example, if you wanted to conduct a survey about food preferences in a school of 1000 students, and you wanted to sample 100 students.

You could use simple random sampling by assigning each student a number from 1 to 1000, then using a random number generator to pick 100 numbers.

The students assigned those numbers would be the ones you survey.

• First, choose the target population that you wish to study and determine your desired sample size. The smaller the sample size the less likely, it can be generalized to the wider research population and is unlikely to be fully representative.
• The list of the people from which the sample is drawn is called the sampling frame. Examples of sampling frames include the electoral register, schools, drug addicts, etc.).
• Then, assign a sequential number to each subject in the sampling frame.
• Next, individuals are selected using an unbiased selection method. Some examples of simple random sampling techniques include lotteries, random computer number generators, or random draws.

Minimizes Bias

It is the least biased sampling method, as every member of the target population has an equal chance of being chosen. The purpose of simple random sampling is to give each individual an equal chance of being chosen.

This is meant to represent a group that is free from researcher bias. Like any sampling technique, there is room for error, but this method is intended to be an unbiased approach.

Representativeness

Random sampling ensures that every member of the target population has an equal chance of being selected. This helps to ensure that the sample is representative of the population, making it more likely that the findings can be generalized to the entire population.

Limitations

Expensive and time-consuming.

It is a very expensive and time-consuming method; it is difficult to get the name of every member of the target population, especially if it is a very large population, so it is rarely used.

Access to respondents

This is actually quite hard to achieve – especially if the parent population is large. Since the participants do not volunteer to participate, it can be challenging for researchers to gain access to respondents when drawing from a large population.

Sampling error

Sampling errors can occur when the sample does not accurately represent the population as a whole. If this occurs, the researcher would need to restart the sampling process.

Other techniques

There are four types of random sampling techniques (simple, stratified, cluster, and systematic random sampling.

Stratified Random Sampling

• In stratified random sampling , researchers will first divide a population into subgroups, or strata, based on shared characteristics and then randomly select among these groups.
• This method is typically used when a population has distinct differences, such as demographics, level of education, or age, and can easily be broken into subgroups.

Cluster Random Sampling

• Similar to stratified random sampling, cluster random sampling begins by dividing a population into smaller groups.
• However, in cluster sampling, researchers use naturally formed groups to divide a large population up into clusters and then select randomly among the clusters to form the sample.
• Examples of these pre-existing groups could include school districts, city blocks, or households.

Systematic Random Sampling

• Systematic random sampling involves taking random samples at regular periodic intervals.
• For example, if you were conducting a survey in a cafeteria, you could give a survey to every sixth customer that comes into the cafeteria.
• A sample is the participants you select from a target population (the group you are interested in) to make generalizations about. As an entire population tends to be too large to work with, a smaller group of participants must act as a representative sample.
• Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics (e.g., gender, ethnicity, socioeconomic level). In an attempt to select a representative sample and avoid sampling bias (the over-representation of one category of participant in the sample), psychologists utilize various sampling methods.
• Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

Hayes, A. (2021). Simple Random Sample. Investopedia. Retrieved from https://www.investopedia.com/terms/s/simple-random-sample.asp

Simple random sample: Definition and examples. Statistics How To. (n.d.). Retrieved from https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/simple-random-sample/

Simple random sampling: Definition, examples, and how to do it. Qualtrics. (2022). Retrieved from https://www.qualtrics.com/experience-management/research/simple-random-sampling/ nce-management/research/simple-random-sampling/

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Simple Random Sampling: Definition and Examples

Simple random sampling is a statistical method in which everyone in a population has an equal chance of being selected into a sample. The sample represents a smaller and more manageable portion of the people that can be studied and analyzed. It’s a fundamental technique to gather data and make inferences about a population.

Simple random sampling is considered a fair and unbiased sample selection method.  This type of sampling is the most straightforward sample selection bias method.

What is Simple Random Sampling?

Simple random sampling is a technique where every item in the population has an even chance and likelihood of being selected. Here, the selection of items entirely depends on luck or probability. Therefore, this sampling technique is also a method of chance.

Simple random sampling is a fundamental method and can easily be a component of a more complex method. The main attribute of this sampling method is that every sample has the same probability of being chosen.

The sample size in a simple random sampling method should ideally be more than a few hundred so that it can be applied appropriately. This method is theoretically simple to understand but difficult to implement practically. Working with a large sample size isn’t an easy task, and it can sometimes be challenging to find a realistic sampling bias frame.

Simple Random Sampling Methods

Researchers follow these methods to select a simple random sample:

• They prepare a list of all the population members initially, and each member is marked with a specific number ( for example, if there are nth members, then they will be numbered from 1 to N).
• Researchers from this population choose random samples using random number tables and random number generator software. Researchers prefer random number generator software, as no human interference is necessary to generate samples.

Two approaches aim to minimize any biases in the process of this method:

01. Method of lottery

Using the lottery method is one of the oldest ways and is a mechanical example of a random sample . Researchers draw numbers from the box randomly to choose samples. In this method, the researcher gives each member of the population a number.

02. Use of random numbers

Using random numbers is an alternative method that also involves numbering the population. A numbered table similar to the one below can help with this sampling technique.

Simple Random Sampling Formula

Consider that a hospital has 1000 staff members and must allocate a night shift to 100 members. All their names will be put in a bucket to be randomly selected. Since each person has an equal chance of being selected. Since we know the population size (N) and sample size (n), the calculation can be as follows:

• P = 1 – {( N – 1 ) / N } . ( N – 2) / ( N – 1) . . . (N-n) / {N – ( n – 1 )}
• Cancelling = 1 – {( N – n ) / N } = n / N = 100 / 1000 = 10%

Simple Random Sampling Steps

Simple random sampling is a crucial method in statistical analysis for drawing unbiased conclusions about a population. Below are the steps to perform simple random sampling to select a sample of 100 employees out of a total of 500 in an organization.

Step 1: Make a List

To start simple random sampling, first, make a complete list of all 500 employees in the organization. It’s important that the list includes the names of every employee to guarantee that each person is considered.

A precise and thorough list is crucial to ensure the sampling accurately reflects the entire population.

Step 2: Assign a Sequential Number

After creating the list of employees, the next thing to do is give each employee a number in order. This is your sampling frame (the list from which you draw your sample). This numbering helps organize the list, making identifying each person in the group easier.

Every employee should have their own number, starting from 1 and going up to n, which is the total number of employees in the organization.

Step 3: Choose Sample Size

Selecting the right sample size is important in simple random sampling. In this situation, we’ve chosen a sample of 100 employees from a total population of 500. It’s essential to pick a sample size that’s large enough for dependable results but still practical for analysis.

Step 4: Use a Random Number Generator

To choose a sample from the group, use a random number generator. First, find the total number of people (Step 2) and decide how many we want in our sample (Step 3).

Then, use a random number table or generator to create 100 different random numbers between 1 and 500. These numbers match the order given to each employee, which helps you pick who will be in the sample.

This method ensures that each employee has an equal opportunity for selection, maintaining fairness and impartiality in sample selection.

It is important to note that Simple Random Sampling is just one of many sampling methods available, and it may not always be the best option for your specific research needs.

Simple Random Sample vs Other Sampling Methods

When thinking about how to sample, people often look at different methods like simple random sampling, stratified sampling, systematic sampling, and cluster sampling. Each method has its pros and cons, so it’s crucial to choose the right one depending on what you’re studying and the features of the group you’re looking at.

Simple vs Stratified Random Sample

The simple random sampling techniques and stratified random sampling have different ways of choosing samples from a population.

• Involves the entire population of data.
• Every person or item is equally likely to be chosen.
• Separates the population into groups with similar characteristics.
• Samples are selected independently from each group.

Simple vs Cluster Sampling

While simple random samples treat each individual in the population as a potential sample unit, cluster sampling involves grouping individuals into clusters or natural units before selecting samples.

• No clusters or divisions within the population.
• Each individual has an equal chance of selection.
• Depends on one or more clusters.
• Groups individuals into clusters, and then samples are selected from these clusters.

Simple vs Systematic Sampling

Systematic sampling involves selecting samples at regular intervals after starting randomly.

• No starting point or predetermined pattern.
• It involves choosing samples at regular intervals after a random start.
• It can be easier to implement but may lead to biased results if patterns exist in the data.

LEARN ABOUT: Purposive Sampling

Simple Random Sampling in Research

Today’s market research projects are much larger and involve an indefinite number of items. It is practically impossible to study every member of the population’s thought process and derive interference from the study.

If, as a researcher, you want to save your time and money, simple random sampling is one of the best probability sampling methods that you can use. Getting data from a sample is more advisable and practical.

Using a census or a sample depends on several factors, such as the type of census, the degree of homogeneity/heterogeneity, costs, time, feasibility of study, the degree of accuracy needed, etc.

Advantages of Simple Random Sampling

Simple random sampling has several advantages, including:

• It is a fair sampling method, and if applied appropriately, it helps reduce any bias involved compared to any other sampling method.
• Since it involves a large sample frame, it is usually easy to pick a smaller sample size from the existing larger population.
• The person conducting the research doesn’t need to have prior knowledge of the data he/ she is collecting. One can ask a question to gather the researcher need not be a subject expert.
• This sampling method is a fundamental method of collecting the data . You don’t need any technical knowledge. You only require essential listening and recording skills.
• Since the population size is vast in this type of sampling method, there is no restriction on the sample size that the researcher needs to create. From a larger population, you can get a small sample quite quickly.
• The data collected using this sampling method is valuable. The higher the number of samples, the better the quality of the data.

Overall, this is a valuable and versatile method for gathering data and making inferences about populations.

Disadvantages of Simple Random Sampling

Simple random sampling has some drawbacks that can affect the relevance of the collected data:

• Sampling errors may happen if the sample doesn’t accurately reflect the intended population.
• Excluding specific groups could lead to skewed results because of imbalanced population demographics.
• Analyzing research results from simple random sampling can be time-consuming and expensive, especially depending on the data’s size and format.
• The sample’s random selection may cause differences in the representation of the population.
• Inaccurate results may arise due to non-response bias when certain groups choose not to participate in the research.

LEARN ABOUT: Survey Sampling

Researchers use simple random sampling in statistical analysis methods valuable for various applications. Selecting a sample of individuals from a population in a random and unbiased manner provides a representative sample and a cost-effective way of gathering data and making inferences about populations.

With QuestionPro, researchers and data analysts can easily and efficiently implement simple random sampling in their research and studies. We are here to help to ensure that the results are accurate.

If you’re a market researcher trying to learn more about your target audience or a social scientist aiming to study a population, Simple Random Sampling with QuestionPro is a dependable and efficient method to explore.

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Simple Random Sampling – Definition & Examples

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In simple random sampling, each member of the target population has an equal chance of being selected, ensuring an unbiased representation. This methodology allows researchers to make generalized conclusions about the population based on the sample, bolstering the reliability and validity of the study’s findings. Moreover, the ease of understanding and implementation makes simple random sampling a popular choice among researchers across various fields.

Inhaltsverzeichnis

• 1 Simple Random Sampling – In a Nutshell
• 2 Definition: Simple random sampling
• 3 When do you use simple random sampling?
• 4 Simple random sampling: 4 Steps

Simple Random Sampling – In a Nutshell

There are several methods of simple random sampling which aim to produce the most accurate sample.

Simple random sampling has many benefits as it generally reduces bias and gives every member of the population an equal chance to participate in a study.

Definition: Simple random sampling

Simple random sampling refers to the process of randomly picking a sample from a population without any prior defined selection process.

Since the sample selection is entirely arbitrary, simple random selection is used in research as an unbiased method of studying subsets in a given population.

When do you use simple random sampling?

Depending on several factors, such as population size, it may be challenging to undertake simple random sampling. Some of the conditions for simple random sampling include:

• A comprehensive list of all the members in the target population
• A reliable method of contacting the members who have been selected for the study
• Adequate time and resources such as manpower, collection materials, and budgetary allocations.

Simple random sampling is used in research cases that involve a large population. It is the best approach in such instances since every sample is picked randomly. Thus, the resulting sample is assumed to be more inclusive of the main themes in the larger population. Additionally, simple random sampling can be used in cases where time and resources are readily available.

Researchers may use a combination of two probability sampling techniques based on the objectives of a case study . For example, simple random sampling may be used to construct the initial sample then systematic sampling may be applied to further distill the sample. The main types of probability sampling used in research include:

Simple random sampling: 4 Steps

Step 1 of simple random sampling: Define the population

• In the study of Teaching Staff in the US, the population equals all the 3.2 million teachers in different capacities within the US.

Step 2 of simple random sampling: Decide on the sample size

You can use standard deviation, confidence interval , and confidence level metrics. The most preferred confidence interval is 0.05 , while the confidence level usually is 0.95 .

If you are unsure of the standard deviation , choose a number such as 0.5 , which can accommodate a range of possibilities. A sample size calculator can then be used to estimate the sample size.

• The Harvard study on well-being and happiness has been studying the lives of 724 men over the last few decades.
• This group of men was identified at a young age from different socioeconomic backgrounds.
• While this sample is small, it accommodates a range of factors such as income, family size, and education.
• These variables are distributed among the study members, offering a detailed report.

Step 3 of simple random sampling: Randomly select your sample

• Each member is assigned a number; these numbers are drawn randomly from a pool.
• Computer software may be used to do the same task.
• The members of the population are tagged with numbers.
• Rearchers then use different number generators to generate random numbers to be used in the sample.
• Other tools used in number generation include the RAND function in Microsoft Excel.
• The World Health Organization stipulates the random sampling of patients on new drug test runs.

Step 4 of simple random sampling: Collect data from your sample

Researchers need to ensure every member selected for sampling is available and willing to participate in the study. If any members fail to co-operate or withdraw from the study, it may interfere with the accuracy of the findings.

• The American Housing Survey invites participants through their website.
• If the recipients fail to respond, a follow-up email and a physical visit may be arranged.
• This ensures that most if not all of the respondents participate in the study to inform policy development.

What are the advantages of simple random sampling?

Simple random sampling reduces the chances of errors from pre-selected members of a sample. It is also easy to carry out as the methods are relatively straightforward.

What are the downsides of simple random sampling?

Simple random sampling may not be applicable where the population is distributed across a large area.

Researchers may also face challenges accessing the sample group.

Additionally, simple random selection may be time-consuming and expensive over a period of time.

What is simple random sampling?

It is a probabilistic method of sample selection.

Members of a population are selected based on homogenous and heterogeneous characteristics.

Researchers use this type of sampling to study defined research goals in a large population.

What are the methods used in simple random sampling?

The main methods used include;

• systematic sampling
• clustered sampling
• stratified sampling

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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

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and behavioral finance. Adam received his master's in economics from The New School for Social Research and his Ph.D. from the University of Wisconsin-Madison in sociology. He is a CFA charterholder as well as holding FINRA Series 7, 55 & 63 licenses. He currently researches and teaches economic sociology and the social studies of finance at the Hebrew University in Jerusalem.

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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Random sampling essay.

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Researchers are usually interested in making some kind of an inference from the data obtained from the sample – a ‘generalization’ of some sort. However, practical considerations typically require the researcher to limit his or her data collection to a sample drawn from a larger population of interest. The ability to make a population inference is going to depend in large part on how the sample was obtained, for the method chosen influences how similar the sample is to the population on all dimensions, characteristics, or features that are likely to influence or be related to the measurement of the variables in the study. When population inference is the goal the researcher is well advised to employ some kind of random sampling method.

Random sampling (also called ‘probability’ or ‘probabilistic’ sampling) requires that the process through which members of the population end up in the sample be determined by chance. Furthermore, for each member of the population, it must be possible to derive the probability of inclusion in the sample (even if you never actually calculate that probability). Random sampling is extremely important when the goal of the research is population inference, for it is the random sampling process that will, over the long haul, produce a sample that represents the population. Although it is possible that, just by chance, a specific sample will be unrepresentative of the population as a whole on one or more relevant dimensions, random sampling ensures that no conscious or unconscious biases will influence who ends up included in the sample.

The most basic form of random sampling is simple random sampling. Here, every unit of the population must have an equal probability of being included in the sample. In order to conduct a simple random sample, the researcher must have some means of identifying who or what is in the population in order to implement a method for making sure that each member has an equal chance of being included. Thus, simple random sampling requires that the investigator have some kind of list of the population prior to sampling – the ‘sampling frame.’ However, for many populations that communication researchers would be interested in sampling from, such lists do not exist.

There are reasons not to use simple random sampling even when it is possible. When conducting a stratified random sample, the population is first split into groups (strata) that are homogeneous on the stratification variable. Then a simple random sample of each stratum is taken. The sample will contain as many members of population in each stratum as you desire, with that number being a function of whether the stratified sampling is done proportionally or non-proportionally.

A related method easily confused with stratified sampling is cluster sampling. To conduct a cluster sample, it must be possible for members of the population to be classified into groups (clusters) in some fashion. When you cluster sample, all you need to have available is the universe of clusters. You randomly sample clusters from the universe of clusters, and for those clusters that are randomly selected, you include each and every cluster member in the sample.

The penetration of the telephone into most households has made sampling of people much easier than in the past. By randomly dialing telephone numbers, it is possible to collect random samples of large populations of people who are geographically dispersed. This approach does not require an enumeration of the members of the population in advance of sampling because it relies on the assumption that most people are attached to at least one phone number.

In practice, random sampling plans are often multistage, mixing sampling methods of different types that are conducted at different stages during the sampling process. For example, a researcher who wanted to collect data by doing face-to-face interviews of a random sample of urban city dwellers of an entire country would find it very difficult to collect a simple random or stratified sample of that population. Even if it were possible to enumerate the population, it might be costprohibitive to travel to the residences of, say, 1,000 different people dispersed across an entire country.

It is important to acknowledge that even if the selection of members of the population for inclusion in a sample is governed by a random process, nonrandom processes can adulterate random samples. For instance, an investigator might select a sample of people randomly from a population of interest, but certain people who are approached for inclusion in the study are likely to choose not to participate. The process that drives that choice may not be a random one (‘nonresponse bias’

Bibliography:

• Frick, R. W. (1998). Interpreting statistical testing: Process and propensity, not population and random sampling. Behavior Research Methods, Instruments, and Computers, 30, 527–535.
• Hayes, A. F. (2005). Statistical methods for communication science. Mahwah, NJ: Lawrence Erlbaum.
• Lacy, S., Riffe, D., Stoddard, S., Martin, H., & Chang, K.-K. (2001). Sample size for newspaper content analysis multi-year studies. Journalism and Mass Communication Quarterly, 78, 836–845.
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• GETTING STARTED
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Simple random sampling

Simple random sampling is a type of probability sampling technique [see our article, Probability sampling , if you do not know what probability sampling is]. With the simple random sample, there is an equal chance ( probability ) of selecting each unit from the population being studied when creating your sample [see our article, Sampling: The basics , if you are unsure about the terms unit , sample and population ]. This article (a) explains what simple random sampling is, (b) how to create a simple random sample, and (c) the advantages and disadvantages of simple random sampling.

Simple random sampling explained

Creating a simple random sample, advantages and disadvantages of simple random sampling.

Imagine that a researcher wants to understand more about the career goals of students at a single university. Let's say that the university has roughly 10,000 students. These 10,000 students are our population ( N ). Each of the 10,000 students is known as a unit (although sometimes other terms are used to describe a unit; see Sampling: The basics ). In order to select a sample ( n ) of students from this population of 10,000 students, we could choose to use a simple random sample.

With simple random sampling, there would an equal chance ( probability ) that each of the 10,000 students could be selected for inclusion in our sample. If our desired sample size was around 200 students, each of these students would subsequently be sent a questionnaire to complete (imagining we choose to collect our data using a questionnaire).

To create a simple random sample, there are six steps : (a) defining the population; (b) choosing your sample size; (c) listing the population; (d) assigning numbers to the units; (e) finding random numbers; and (f) selecting your sample.

• STEP ONE: Define the population
• STEP TWO: Choose your sample size
• STEP THREE: List the population
• STEP FOUR: Assign numbers to the units
• STEP FIVE: Find random numbers
• STEP SIX: Select your sample

STEP ONE Define the population

In our example, the population is the 10,000 students at the single university. The population is expressed as N. Since we are interested in all of these university students, we can say that our sampling frame is all 10,000 students. If we were only interested in female university students, for example, we would exclude all males in creating our sampling frame, which would be much less than 10,000 students.

STEP TWO Choose your sample size

Let's imagine that we choose a sample size of 200 students. The sample is expressed as n . This number was chosen because it reflects the limit of our budget and the time we have to distribute our questionnaire to students. However, we could have also determined the sample size we needed using a sample size calculation , which is a particularly useful statistical tool. This may have suggested that we needed a larger sample size; perhaps as many as 400 students.

STEP THREE List the population

To select a sample of 200 students, we need to identify all 10,000 students at the university. If you were actually carrying out this research, you would most likely have had to receive permission from Student Records (or another department in the university) to view a list of all students studying at the university. You can read about this later in the article under Disadvantages of simple random sampling .

STEP FOUR Assign numbers to the units

We now need to assign a consecutive number from 1 to N , next to each of the students. In our case, this would mean assigning a consecutive number from 1 to 10,000 (i.e., N = 10,000; the population of students at the university).

STEP FIVE Find random numbers

Next, we need a list of random numbers before we can select the sample of 200 students from the total list of 10,000 students. These random numbers can either be found using random number tables or a computer program that generates these numbers for you.

STEP SIX Select your sample

Finally, we select which of the 10,000 students will be invited to take part in the research. In this case, this would mean selecting 200 random numbers from the random number table . Imagine the first three numbers from the random number table were:

We would select the 11 th , 9,292 nd and 2,001 st students from our list to be part of the sample. We keep doing this until we have all 200 students that we want in our sample.

The advantages and disadvantages of simple random sampling are explained below. Many of these are similar to other types of probability sampling technique, but with some exceptions. Whilst simple random sampling is one of the 'gold standards' of sampling techniques, it presents many challenges for students conducting dissertation research at the undergraduate and master's level.

Advantages of simple random sampling

The aim of the simple random sample is to reduce the potential for human bias in the selection of cases to be included in the sample. As a result, the simple random sample provides us with a sample that is highly representative of the population being studied, assuming that there is limited missing data.

Since the units selected for inclusion in the sample are chosen using probabilistic methods , simple random sampling allows us to make generalisations (i.e., statistical inferences ) from the sample to the population . This is a major advantage because such generalisations are more likely to be considered to have external validity .

Disadvantages of simple random sampling

A simple random sample can only be carried out if the list of the population is available and complete .

Attaining a complete list of the population can be difficult for a number of reasons:

Even if a list is readily available, it may be challenging to gain access to that list. The list may be protected by privacy policies or require a lengthy process to attain permissions.

There may be no single list detailing the population you are interested in. As a result, it may be difficult and time consuming to bring together numerous sub-lists to create a final list from which you want to select your sample. As an undergraduate and master?s level dissertation student, you may simply not have sufficient time to do this.

Many lists will not be in the public domain and their purchase may be expensive; at least in terms of the research funds of a typical undergraduate or master's level dissertation student.

In terms of human populations (as opposed to other types of populations; see the article: Sampling: The basics ), some of these populations will be expensive and time consuming to contact, even where a list is available. Assuming that your list has all the contact details of potential participants in the first instance, managing the different ways (e.g., postal, telephone, email) that may be required to contact your sample may be challenging, not forgetting the fact that your sample may also be geographical scattered.

In the case of human populations, to avoid potential bias in your sample, you will also need to try and ensure that an adequate proportion of your sample takes part in the research. This may require re-contacting non-respondents, can be very time consuming, or reaching out to new respondents.

If you are an undergraduate or master's level dissertation student considering using simple random sampling , you may also want to read more about how to put together your sampling strategy [see the section: Sampling Strategy ].

Simple Random Technique’s Application Essay

Simple random (SR) technique, as a type of probability sample, is aimed at specifying and targeting the population. Is an SR sample, a researcher designs a sampling frame and then employs a purely random process to choose cases (Neuman 255). As a result, each of the sampling elements obtains an equal opportunity of being selected. The difficulty with SR sampling is that the researcher needs to locate the particular sampled feature determined by a random process (Neuman 255).

Thus, in the present scenario, it may be necessary to contact the specific household several times to engage it in research. Opting for an SR sample is appropriate in the given case since it will enable the researcher to mimic the US population in terms of the proportion of different ethnicities.

Other potential sampling plans that might be used are convenience and quota sampling. Convenience sampling, which is also called accidental or haphazard, is less suitable for the present study. In this approach, cases are readily available and easy to reach (Neuman 248). However, this method is reported to produce “very nonrepresentative samples” quite frequently (Neuman 248). An example of such a sample is interviewing people in the street when only those whom the researcher finds easily accessible are selected.

Another probable plan, quota sampling, involves identifying relevant groups among the whole population to “capture diversity among units” (Neuman 249). Weaknesses of quota sampling include capturing only a few features and producing an inaccurate reflection of the population’s proportions (Neuman 249). Thus, simple random sampling is the most relevant choice for the given study.

Neuman, W. Lawrence. Social Research Methods: Qualitative and Quantitative Approaches . 7th ed., Pearson, 2014.

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• 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.

Table of contents

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.

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.

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.

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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Most books on essay writing will supply you with a number of model essays—collect some of these as they are great teachers! No matter what the topic is, you will see similarities between your writing tasks and these model essays. This is because many features of writing are common across subject areas. In some subjects (e.g. Law, Economics, Psychology and others), it is very useful to find subject -specific essay models as you can use these to work out the ‘peculiarities’ of writing for that subject area.

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The following five paragraph essay has paragraph labels to show the parts of an academic essay. (Note: This essay does not contain authentic references and has been written specifically to use for this teaching task.)

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Using assignment essays for assessment supports student learning better than the traditional examination system. It is considered that course-work assignment essays can lessen the extreme stress experienced by some students over ‘sudden-death’ end of semester examinations:

If we insist that all students write about everything they have learned in their study courses at the same time and in the same place (e.g. in examinations), we are not giving all of our students equal opportunities. Some students are not daunted by the exam experience while others suffer ‘exam nerves’ and perform at the lowest level of their capabilities. (Wonderland University, 2006, p. 4)

Additionally, Jones et al. (2004, pp. 36-37) propose that assignment essays can be used to assess student learning mid-course and so provide them with helpful feedback before they are subjected to the exam experience. Exams only provide students with a mark rather than specific feedback on their progress. Therefore, setting assignment essays for a substantial part of student assessment is a much fairer approach than one-off examination testing.

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Bloggs, J. (2003).  Linking teaching, learning and succeeding in higher education . London: Bookworld.

Jinx, J.M. (2004). Student essay writing.  Journal of Research in University Education, 9 (2), 114-125.

Jones, J., Smith, P.L., Brown, K., Zong J., Thompson, K., & Fung, P.A. (2004).  Helpline: Essays and the university student . Tokyo: Courtyard Printers.

Sankey, J.M., & Liger, T.U. (2003).  Learning to write essays  [CD-ROM]. Sydney: Wonderland University.

Taylor, G. (1989).  The student’s writing guide for the arts and social sciences . Cambridge: Cambridge University Press.

Wonderland University. (2006).  Attributes of a university graduate . doi:10.1098/063-112

Yang, S., & Baker, O.E. (2005).  Essay writing and the tertiary student . Melbourne: Diamond Press.

Zapper, Y. (2006). Learning essay writing. In F.T. Fax & Y. Phoney (Eds.),  Learning Experiences at University  (pp. 55-70). Calcutta: Academic Scholar Press.

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AQA Business Studies Unit 1 Essay

Aqa business studies unit 1.

(a). What is meant by the term random sample?

A sample is an item chosen from the total population, which is being studied. Therefore, a random sample is the number chosen, and it involves components that are unpredictable. This is selection of a few items to represent the entire population. The sample chosen does not represent the population from which it was drawn. This was an estimate done through survey and information from franchisors, which Tom estimated that it would be about 77000 customers in the first year according to his analysis (Dayananda, et. al. 56).

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Example Of Essay On Uses Of Statistical Information

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1(a) What is meant by the term random sample (line 34)?

The term random sample means a part of a population chosen for investigation without following any specific scientific pattern. The elements of the sample are chosen without following a definite pattern.

1(b) State the formula for calculating ‘market share’ percentage (line 50)?

Market share percentage=

1 (c) Tom believes that the level of spending per visit by his customers will stay the same in 2011 and 2012. Based on the information in the case study, how many customers is he anticipating in 2012?

Number of customers expected=8350 customers.

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Home — Essay Samples — Life — Empathy — Random Act Of Kindness

Random Act of Kindness

• Categories: Community Compassion Empathy

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Words: 527 |

Published: Mar 19, 2024

Words: 527 | Page: 1 | 3 min read

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