### What Is Random Selection In Psychology?

- Sabrina Sarro
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Random Selection Random Selection is a process of gathering (in a truly random way) a representative sample for a particular study. Having a random sample is important because the scientist wants to generalize his or her findings to the whole population without actually testing the whole population.

- In order to achieve this, the scientist identifies a population or group to study and randomly selects people (it could also be an item or animal, etc.) to be in the study.
- Random means the people are chosen by chance, i.e.
- Each person has the same probability of being chosen like picking names out of a hat.

When you have a truly random sample, you reduce the chance that the results are due to factors of the participants in the study. : Random Selection

Contents

- 1 What is random selection in psychology with example?
- 2 What is random selection in research example?
- 3 What is random vs non random selection?
- 4 Why is random selection important in psychology?
- 5 What is a good example of random sampling?
- 6 What are the advantages and disadvantages of random selection?
- 7 What is random sample in simple terms?
- 8 Which of the following is the best example of random selection?

## What is random selection in psychology with example?

Random Selection – In order to generalize the results of an experiment to a larger group, it is important to choose a sample that is representative of the qualities found in that population. For example, if the total population is 51% female and 49% male, then the sample should reflect those same percentages.

- Choosing a representative sample is often accomplished by randomly picking people from the population to be participants in a study.
- Random selection means that everyone in the group stands an equal chance of being chosen.
- Once a pool of participants has been selected, it is time to assign them into groups.

By randomly assigning the participants into groups, the experimenters can be fairly sure that each group will be the same before the independent variable is applied. Participants might be randomly assigned to the control group, which does not receive the treatment in question.

Or they might be randomly assigned to the experimental group, which does receive the treatment. Random assignment increases the likelihood that the two groups are the same at the outset. That way any changes that result from the application of the independent variable can be assumed to be the result of the treatment of interest.

### What is the meaning of random selection?

Difference between Random Selection and Random Assignment Difference between Random Selection and Random Assignment and are commonly confused or used interchangeably, though the terms refer to entirely different processes. Random selection refers to how sample members (study participants) are selected from the population for inclusion in the study.

Random assignment is an aspect of in which study participants are assigned to the treatment or control group using a random procedure. Random selection requires the use of some form of random sampling (such as stratified, in which the population is sorted into groups from which sample members are chosen randomly).

Random sampling is a probability sampling method, meaning that it relies on the laws of probability to select a sample that can be used to make inference to the population; this is the basis of statistical tests of, Aligning theoretical framework, gathering articles, synthesizing gaps, articulating a clear methodology and data plan, and writing about the theoretical and practical implications of your research are part of our comprehensive dissertation editing services.

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Random assignment takes place following the selection of participants for the study. In a true experiment, all study participants are randomly assigned either to receive the treatment (also known as the stimulus or intervention) or to act as a control in the study (meaning they do not receive the treatment).

Although random assignment is a simple procedure (it can be accomplished by the flip of a coin), it can be challenging to implement outside of controlled laboratory conditions. A study can use both, only one, or neither. Here are some examples to illustrate each situation: A researcher gets a list of all students enrolled at a particular school (the population).

Using a random number generator, the researcher selects 100 students from the school to participate in the study (the random sample). All students’ names are placed in a hat and 50 are chosen to receive the intervention (the treatment group), while the remaining 50 students serve as the control group.

This design uses both random selection and random assignment. A study using only random assignment could ask the principle of the school to select the students she believes are most likely to enjoy participating in the study, and the researcher could then randomly assign this sample of students to the treatment and control groups.

In such a design the researcher could draw conclusions about the effect of the intervention but couldn’t make any inference about whether the effect would likely to be found in the population. A study using only random selection could randomly select students from the overall population of the school, but then assign students in one grade to the intervention and students in another grade to the control group.

- While any data collected from this sample could be used to make inference to the population of the school, the lack of random assignment to be in the treatment or control group would make it impossible to conclude whether the intervention had any effect.
- Random selection is thus essential to external validity, or the extent to which the researcher can use the results of the study to generalize to the larger population.

Random assignment is central to internal validity, which allows the researcher to make causal claims about the effect of the treatment. Nonrandom assignment often leads to non-equivalent groups, meaning that any effect of the treatment might be a result of the groups being different at the outset rather than different at the end as a result of the treatment.

## What is random selection in research example?

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.

## What is random vs non random selection?

Difference between Random Sampling and Non-random Sampling There are mainly two methods of sampling which are random and non-random sampling. Random sampling is referred to as that sampling technique where the probability of choosing each sample is equal.

- The sample that is chosen randomly is an unbiased representation of the total population.
- If at all, the sample chosen does not represent the population, it leads to sampling error.
- Non-random sampling is a sampling technique where the sample selection is based on factors other than just random chance.

In other words, non-random sampling is biased in nature. Here, the sample will be selected based on the convenience, experience or judgment of the researcher. Following are some of the points of difference between random sampling and non-random sampling

Random Sampling | Non-random Sampling |

Definition | |

Random sampling is a sampling technique where each sample has an equal probability of getting selected | Non-random sampling is a sampling technique where the sample selected will be based on factors such as convenience, judgement and experience of the researcher and not on probability |

Biases involved in Sampling | |

Random sampling is unbiased in nature | Non-random sampling is biased in nature |

Based on | |

Based on probability | Based on other factors such as convenience, judgement and experience of researcher but, not based on probability |

Representation of Population | |

Random sampling is representative of the entire population | Non-random sampling lacks the representation of the entire population |

Chances of Zero Probability | |

Never | Zero probability can occur |

Complexity | |

Random sampling is the most simple sampling technique | Non-random sampling method is a somewhat complex sampling technique |

This was all about the concept of difference between random sampling and non-random sampling, which is important for Commerce students. For more such articles, stay tuned to BYJU’S. : Difference between Random Sampling and Non-random Sampling

## Why is random selection important in psychology?

The primary purpose of random sampling in psychology is not to eliminate error, which is impossible, but to make error so random it does not skew results. By error, this means the influence of a confounding variable or anything unintentionally altering the results being measured.

## What is a good example of random sampling?

About this Release Contents

SAMPLING METHODS If you survey every person or a whole set of units in a population you are taking a census, However, this method is often impracticable; as it’s often very costly in terms of time and money. For example, a survey that asks complicated questions may need to use trained interviewers to ensure questions are understood.

- This may be too expensive if every person in the population is to be included.
- Sometimes taking a census can be impossible.
- For example, a car manufacturer might want to test the strength of cars being produced.
- Obviously, each car could not be crash tested to determine its strength! To overcome these problems, samples are taken from populations, and estimates made about the total population based on information derived from the sample.

A sample must be large enough to give a good representation of the population, but small enough to be manageable. In this section the two major types of sampling, random and non-random, will be examined. RANDOM SAMPLING In random sampling, all items have some chance of selection that can be calculated.

simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling.

SIMPLE RANDOM SAMPLING With simple random sampling, each item in a population has an equal chance of inclusion in the sample. For example, each name in a telephone book could be numbered sequentially. If the sample size was to include 2,000 people, then 2,000 numbers could be randomly generated by computer or numbers could be picked out of a hat.

A Tattslotto draw is a good example of simple random sampling. A sample of 6 numbers is randomly generated from a population of 45, with each number having an equal chance of being selected.

The advantage of simple random sampling is that it is simple and easy to apply when small populations are involved. However, because every person or item in a population has to be listed before the corresponding random numbers can be read, this method is very cumbersome to use for large populations.

- SYSTEMATIC SAMPLING Systematic sampling, sometimes called interval sampling, means that there is a gap, or interval, between each selection.
- This method is often used in industry, where an item is selected for testing from a production line (say, every fifteen minutes) to ensure that machines and equipment are working to specification.

Alternatively, the manufacturer might decide to select every 20th item on a production line to test for defects and quality. This technique requires the first item to be selected at random as a starting point for testing and, thereafter, every 20th item is chosen.

This technique could also be used when questioning people in a sample survey. A market researcher might select every 10th person who enters a particular store, after selecting a person at random as a starting point; or interview occupants of every 5th house in a street, after selecting a house at random as a starting point.

It may be that a researcher wants to select a fixed size sample. In this case, it is first necessary to know the whole population size from which the sample is being selected. The appropriate sampling interval, I, is then calculated by dividing population size, N, by required sample size, n, as follows: I = N/n EXAMPLE If a systematic sample of 500 students were to be carried out in a university with an enrolled population of 10,000, the sampling interval would be: I = N/n = 10,000/500 =20 Note: if I is not a whole number, then it is rounded to the nearest whole number. All students would be assigned sequential numbers. The starting point would be chosen by selecting a random number between 1 and 20. If this number was 9, then the 9th student on the list of students would be selected along with every following 20th student.

The sample of students would be those corresponding to student numbers 9, 29, 49, 69,,9929, 9949, 9969 and 9989. The advantage of systematic sampling is that it is simpler to select one random number and then every ‘Ith’ (e.g.20th) member on the list, than to select as many random numbers as sample size.

It also gives a good spread right across the population. A disadvantage is that you may need a list to start with, if you wish to know your sample size and calculate your sampling interval. STRATIFIED SAMPLING A general problem with random sampling is that you could, by chance, miss out a particular group in the sample.

However, if you form the population into groups, and sample from each group, you can make sure the sample is representative. In stratified sampling, the population is divided into groups called strata. A sample is then drawn from within these strata. Some examples of strata commonly used by the ABS are States, Age and Sex.

Other strata may be religion, academic ability or marital status. EXAMPLE

The committee of a school of 1,000 students wishes to assess any reaction to the re-introduction of Pastoral Care into the school timetable. To ensure a representative sample of students from all year levels, the committee uses the stratified sampling technique.

In this case the strata are the year levels. Within each strata the committee selects a sample. So, in a sample of 100 students, all year levels would be included. The students in the sample would be selected using simple random sampling or systematic sampling within each strata,

- Stratification is most useful when the stratifying variables are simple to work with, easy to observe and closely related to the topic of the survey.
- An important aspect of stratification is that it can be used to select more of one group than another.
- You may do this if you feel that responses are more likely to vary in one group than another.

So, if you know everyone in one group has much the same value, you only need a small sample to get information for that group; whereas in another group, the values may differ widely and a bigger sample is needed. If you want to combine group level information to get an answer for the whole population, you have to take account of what proportion you selected from each group (see ‘Bias in Estimation’ ). CLUSTER SAMPLING It is sometimes expensive to spread your sample across the population as a whole. For example, travel can become expensive if you are using interviewers to travel between people spread all over the country. To reduce costs you may choose a cluster sampling technique.

Cluster sampling divides the population into groups, or clusters. A number of clusters are selected randomly to represent the population, and then all units within selected clusters are included in the sample. No units from non-selected clusters are included in the sample. They are represented by those from selected clusters.

This differs from stratified sampling, where some units are selected from each group. Examples of clusters may be factories, schools and geographic areas such as electoral sub-divisions. The selected clusters are then used to represent the population. EXAMPLE

Suppose an organisation wishes to find out which sports Year 11 students are participating in across Australia. It would be too costly and take too long to survey every student, or even some students from every school. Instead, 100 schools are randomly selected from all over Australia.

These schools are considered to be clusters. Then, every Year 11 student in these 100 schools is surveyed. In effect, students in the sample of 100 schools represent all Year 11 students in Australia. Cluster sampling has several advantages: reduced costs, simplified field work and administration is more convenient.

Instead of having a sample scattered over the entire coverage area, the sample is more localised in relatively few centres (clusters). Cluster sampling’s disadvantage is that less accurate results are often obtained due to higher sampling error (see section Information – Problems with Using ) than for simple random sampling with the same sample size.

In the above example, you might expect to get more accurate estimates from randomly selecting students across all schools than from randomly selecting 100 schools and taking every student in those chosen. MULTI-STAGE SAMPLING Multi-stage sampling is like cluster sampling, but involves selecting a sample within each chosen cluster, rather than including all units in the cluster.

- Thus, multi-stage sampling involves selecting a sample in at least two stages.
- In the first stage, large groups or clusters are selected.
- These clusters are designed to contain more population units than are required for the final sample.
- In the second stage, population units are chosen from selected clusters to derive a final sample.

If more than two stages are used, the process of choosing population units within clusters continues until the final sample is achieved. EXAMPLE

An example of multi-stage sampling is where, firstly, electoral sub-divisions (clusters) are sampled from a city or state. Secondly, blocks of houses are selected from within the electoral sub-divisions and, thirdly, individual houses are selected from within the selected blocks of houses.

The advantages of multi-stage sampling are convenience, economy and efficiency. Multi-stage sampling does not require a complete list of members in the target population, which greatly reduces sample preparation cost. The list of members is required only for those clusters used in the final stage.

### Is random selection really random?

Random(ish) sampling: balancing the ideal and the real So you’ve got your survey hot off the presses and ready to launch. There’s just one problemwho do you send it to? Random sampling is a way to sample in which everyone in the population has a chance of being chosen for the sample, and whoever’s picked is chosen completely at random.

This is great because there’s no bias – some people aren’t more likely to be picked than others. Plus, anyone from your population could conceivably be picked for your sample, so you can be more confident your results match what your population really thinks. The simplest way to understand random sampling is to think of someone pulling slips of paper at random out of a hat.

Every slip of paper in the hat has an equal chance of being plucked out. So if every slip of paper has a name on it, every name has an equal chance of getting picked. That means that it is “random” which names get picked.

Approach random people on a random selection of streets at random times. Call randomly generated phone numbers at random times of the day. Mail out letters to randomly selected addresses from randomly selected regions. Email an online survey to randomly generated email addresses.

But, wait, there are some problems with this Let’s go back to our hat example. Do all the names in the hat really have an equal chance of being picked? Wellhave you ever picked a slip of paper out of a hat? No one takes the slip of paper right on the top! You stick your hand way in there and grab one from at least the middle.

Approaching people randomly is not only a little creepy but also unlikely to actually be random. Especially because the person you have doing the actual approaching is going to bias who says yes to the survey and potentially even the answers they give. (Don’t believe me? Try it yourself. Call up Kanye West and ask him to hand out a survey on one street—and then have your grandma hand out a survey on another street. Watch what happens.) People who answer phone calls and letters are a very particular subset of people these days and these methods may not be able to estimate some types of behavior. Methods 1-3 can also be very time consuming and very expensive and you’ve got yourself in a bit of a pickle. Going the email route, chances are you’ll be sent directly to a SPAM folder and people will never see your email at all. Chances are you’ll also get lots of angry emails about spamming random strangers.

In a perfect world, everyone in our population would have a chance of being picked in our sample—in practice, that is virtually impossible. With surveys, as with many other things in life, you’ll need to strike a balance between what is ideal and what is possible in the real world. We like to call it “random(ish) sampling.” Here are some suggestions we have for random(ish) sampling

If you have a concrete “sampling frame” (a list of the names of the people in your population), you can use a random number generator to select which people to pick to survey from your list. (Want help with this? to do this in,)If you don’t have a concrete sampling frame, you could vary who is approaching strangers on the street to ask them to fill out the survey. Have Kanye and your grandma go survey collecting together—and have them bring some friends to help too. The biases that each of them bring to data collection are likely to balance out.Send your survey out more than one way. Mail it, call people, send it online, shout it from the rooftops! Okay the last one was a joke, but the diversity of recruiting methods can help get different types of people to respond.Post your online survey on different types of websites where different types of people might see it. People who read political blogs may be different people from those who read fashion blogs. This is a nice alternative to the mass email that is likely to hit everyone’s spam filter. You can cast a wide net to draw in a diverse crowd without clogging anyone’s inbox.

: Random(ish) sampling: balancing the ideal and the real

#### Why is random selection not random?

But it’s not random either! – Understanding Evolution So it is a misconception to view natural selection as a process that perfects organisms. At the opposite extreme, natural selection is sometimes interpreted as a process. This is also a misconception.

- The genetic variation that occurs in a population because of mutation is random — but selection acts on that variation in a very non-random way: genetic variants that aid survival and reproduction are much more likely to become common than variants that don’t.
- Natural selection is not random! A population of organisms undergoes random mutation and non-random selection.

The result is non-random evolutionary change. : But it’s not random either! – Understanding Evolution

## What are the advantages and disadvantages of random selection?

Key Takeaways –

A simple random sample is one of the methods researchers use to choose a sample from a larger population.This method works if there is an equal chance that any of the subjects in a population will be chosen. Researchers choose simple random sampling to make generalizations about a population. Major advantages include its simplicity and lack of bias.Among the disadvantages are difficulty gaining access to a list of a larger population, time, costs, and that bias can still occur under certain circumstances.

## What is random sample in simple terms?

What is simple random 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.

## Which of the following is the best example of random selection?

Which of the following is the best example of random selection? Arbitrarily selecting names from everyone in the population to determine who will be in the study.

### What is an example of random allocation?

This is random allocation, or random assignment, and all the methods used for random selection can be used for random allocation. For example, a pharmacy company may choose every other name on its sample list to choose who gets the new medication and who gets the placebo (systematic).