Probability sample:
Probability sampling, also known as random sampling, is the independent, random selection of participants based on probability theory, in the sense that it is controlled solely by chance. Probability-based sampling is beneficial because it increases the likelihood of getting a more representative sample of the population you're interested in. For a sample to be truly random, each participant drawn from the population of interest must have an equal chance of being selected, and a selected participant must occur independently of every other selected participant.
There are several subtypes of probability sampling, including systematic simple random sampling, stratified random sampling, and cluster sampling. We'll explore each of these sample types with examples based on a controversial question: does pineapple go with pizza?
Systematic rehearsals:
A systematic sample is a type of probability sample, but systematic samples are not random. In systematic sampling, a rule is specified and applied to selected participants. A common practice is to select every "nth" person for the sample.
For our pizza example, suppose you want to select a sample of 25 out of 100 customers who show up at a pizzeria and interview all 4 incoming customers. Each guest is given a number from 1 to 100 and upon entering asks all 4 people if pineapple is the place for pizza. If you select all 4 people you will get a probability sample as 25% of the guests were selected. This sample is not random, however, as any guest other than number 4, 8, 12, etc. has no chance of being selected.
Simple spot checks:
Simple random samples are random samples selected from the population of interest where each participant in the sample has an equal probability of being selected compared to the next participant. Simple random sampling is generally used when the sample population is relatively homogeneous or similar. The selection of this population is based on purely random methods, such as drawing lots.
Continuing with our pizza example, let's assume most of the guests at the pizza party are pineapple lovers and each guest should be assigned a number from 1 to 100. While your colleague hands out the numbers to the guests, stand in line and find a number at random. Number Generator Once each guest has a number, it generates a random number between 1 and 100 and asks the guest who matches the generated number if the pineapple belongs on the pizza. Repeat this 25 times until you have a sample of 25 guests out of 100 participants. You can then compare the answers you get and see what the consensus is among pineapple lovers on whether it should be put on pizza. This is a probability sample because 25% of the guests were selected, and it is a random sample because there is an equal chance of being selected at random. A simple random selection was used as the sample was relatively homogeneous as most of the guests are known pineapple lovers.
Stratified Samples:
A stratified random sample is a random sample that represents two or more groups from your interest group. Stratified random sampling is most commonly used when the sample is relatively heterogeneous or there are notable subgroups. This involves dividing your population into smaller groups and randomly selecting a sample from each. Essentially you are treating it as if there were two populations. Common examples are stratification by age, gender, or ethnicity.
Regarding our pizza example, let's say our pizza party has 55 women and 45 men, and you have reason to believe that both genders will react differently to the question of whether or not there should be pineapple on the pizza. To be representative and for a 25% sample, you determine that you need to interview 14 women and 11 men. You randomly assign each woman a number from 1 to 55 and each man a number from 1 to 45. Starting with the female group you jump into the random number generator as before and generate a number between 1 and 55 and ask the guest who matches the generated number if the pineapple belongs on the pizza. You repeat this 14 times to complete your feminine pattern. When finished, repeat the entire process for men. We can then compare women's responses to men's to see if one sex prefers pineapple on pizza more than the other sex. Like a simple random sample, this example is a probability sample because 25% of the guests were selected from each subgroup, and a random sample because the probability of a random selection is equal. Stratified random sampling was used as the sample was heterogeneous as there were males and females.
Random selection by clusters:
Cluster sampling occurs when populations of interest are randomly selected. Cluster sampling usually takes place in two stages: In the first stage, the population of interest is divided into known clusters. Then, in the second stage, multiple clusters are randomly selected and participants within each of the selected clusters are randomly selected to form the final sample.
Suppose our 100-diner pizzeria has groups of guests who prefer different types of pizza dough: thin, medium, thick, cheese-based, hot-dog-based, and gluten-free. Assign each group a number from 1 to 6 and, using a random number generator, determine that with a sample size of approximately 25%, you will sample guests from groups who prefer thin pizza base and cheese base. You would then proceed in the same way as with stratified random sampling, since you now have two groups to sample. Each individual in the thin crust and cheese crust groups is given numbers, and 25% of each group are randomly selected via random number generation and asked if they think the pineapple belongs on the pizza. As before, this is a probability sample because 25% of the guests were selected from each group, and it is random because the probability of being randomly selected from each group is equal. However, the cluster sample is not necessarily as representative as the stratified random sample because not all clusters were examined.
Important Notes:
Probability sampling is beneficial because it reduces sampling bias and demonstrates the diversity in your sample (and therefore in the population). Independent random sampling is also often an assumption of many tests of inferential statistics; Therefore, if this assumption is not met, certain types of analysis cannot be performed. However, it is important to remember that while probability sampling is preferred, how you sample your population of interest depends on your research question and study design. And most importantly, yes, pineapple has a place on pizza! ;)
Useful references:
- Australian Bureau of Statistics (2021). pattern design.
- Health Awareness (2021). population sampling method.
how to do a search How to write poll questions How to increase the response rate of surveys What is demographic data? correlation coefficients What is sampling? What is the sample size? Open vs. closed questions How to conduct an opinion poll quantitative data statistical significance Questions with multiple answers
FAQs
What is sampling? A step by step introduction? ›
A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.
What is sampling in short answer? ›A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.
What is step by step sampling? ›An operational sampling process can be divided into seven steps as given below: Defining the target population. Specifying the sampling frame. Specifying the sampling unit. Selection of the sampling method.
What is sampling in research introduction? ›Sampling is the process of selecting a group of individuals from a population to study them and characterize the population as a whole. The population includes all members from a specified group, all possible outcomes or measurements that are of interest. The exact population will depend on the scope of the study.
What is the sampling method quizlet? ›a subset of a population that is used to study the population as a whole. elements. the individual members of the population whose characteristics are to be measured.
What is sampling and its purpose? ›Definition : Sampling is the process by which inference is made to the whole by examining a part. Purpose of Sampling. The purpose of sampling is to provide various types of statistical information of a qualitative or quantitative nature about the whole by examining a few selected units.
What is sampling and explain its need? ›Sampling saves money by allowing researchers to gather the same answers from a sample that they would receive from the population. Non-random sampling is significantly cheaper than random sampling, because it lowers the cost associated with finding people and collecting data from them.
What is 2 step sampling method? ›In the two-stage sampling design the population is partitioned into groups, like cluster sampling, but in this design new samples are taken from each cluster sampled. The clusters are the first stage units to be sampled, called primary or first sampling units and denoted by SU1.
What is sampling summary? ›Summary. Sampling is a technique where a small proportion of data is selected at random out of a population and is used to estimate the characteristics of the whole population. The outcome from the sampling can be close or similar to that of the result of using the population data set.
Why is it called sampling? ›The term sampling was coined in the late 1970s by the creators of the Fairlight CMI, a synthesizer with the ability to record and playback short sounds. As technology improved, cheaper standalone samplers with more memory emerged, such as the E-mu Emulator, Akai S950 and Akai MPC.
How do you write a sampling? ›
You need to: (1) describe what you are studying, including the units involved in your sample and the target population; (2) explain the types of sampling technique available to you; (3) state and describe the sampling strategy you used; and (4) justify your choice of sampling strategy.
What is the best sampling method? ›Random samples are the best method of selecting your sample from the population of interest. The advantages are that your sample should represent the target population and eliminate sampling bias.
What is one sampling method? ›In non-probability sampling, the sample is selected based on non-random criteria, and not every member of the population has a chance of being included. Common non-probability sampling methods include convenience sampling, voluntary response sampling, purposive sampling, snowball sampling, and quota sampling.
What is the first step in random sampling? ›Step 1: Define the population
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.
Why are samples used in research? Samples are used to make inferences about populations. Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.
Why is sampling so important in research? ›Studies are conducted on samples because it is usually impossible to study the entire population. Conclusions drawn from samples are intended to be generalized to the population, and sometimes to the future as well. The sample must therefore be representative of the population.
What is used for sampling? ›Sampling is a tool that is used to indicate how much data to collect and how often it should be collected. This tool defines the samples to take in order to quantify a system, process, issue, or problem. To illustrate sampling, consider a loaf of bread. How good is the bread?
What are the different sampling methods? ›There are five types of sampling: Random, Systematic, Convenience, Cluster, and Stratified.
What is sampling data? ›In data analysis, sampling is the practice of analyzing a subset of all data in order to uncover the meaningful information in the larger data set.
What is a sampling plan? ›A sampling plan is a detailed outline of which measurements will be taken at what times, on which material, in what manner, and by whom.
What is a multiple sampling plan? ›
A multiple sampling plan is an extension of the double sampling plans where more than two samples are needed to reach a conclusion. The advantage of multiple sampling is smaller sample sizes.
What is an example of multiple sampling? ›For example, a researcher wants to understand pet feeding habits among people living in the USA. For this, he/she requires a sample size of 200 respondents. The researcher selects 10 states out of 50 at random. Further, he/she randomly picks out 5 districts per state.
What are the three elements of sampling? ›In other words, the sampling process involves three main elements – selecting the sample, collecting the information, and also making inferences about the population.
What is sampling and types of sampling? ›In statistics, sampling is the process of selecting a subset of data from a larger dataset. There are two main types of sampling: probability sampling and non-probability sampling. The main difference between the two types of sampling is how the sample is selected from the population.
What are the 3 main ideas of sampling? ›- Selecting a sample of the population.
- Selecting the sample randomly from the population.
- Obtaining a large sample, regardless of the size of the population sampled.
Sampling is a process in statistical analysis where researchers take a predetermined number of observations from a larger population. The method of sampling depends on the type of analysis being performed, but it may include simple random sampling or systematic sampling.
How did sampling begin? ›It's a widely accepted view that hip-hop was responsible for the creation of sampling. There were early examples of sampling being birthed in the early 70s at hip-hop parties in the Bronx, where DJs like Kool Herc would spin funk and soul records, while MCs rapped over sections of these records live.
How did sampling start? ›Originating in its modern form in the '70s, sampling was an abstract concept to major labels who interpreted it as stealing. But for broke hip-hop producers attempting to emulate Grandmaster Flash, sampling was a quick, easy and (crucially) cheap way to make beats.
What are the 3 steps in a sampling plan? ›- identify the parameters to be measured, the range of possible values, and the required resolution.
- design a sampling scheme that details how and when samples will be taken.
- select sample sizes.
- design data storage formats.
- assign roles and responsibilities.
There are four primary, random (probability) sampling methods – simple random sampling, systematic sampling, stratified sampling, and cluster sampling.
What are the two types of sampling methods? ›
Sampling in market action research is of two types – probability sampling and non-probability sampling. Let's take a closer look at these two methods of sampling. Probability sampling: Probability sampling is a sampling technique where a researcher selects a few criteria and chooses members of a population randomly.
What are the 5 ways of sampling? ›There are five types of sampling: Random, Systematic, Convenience, Cluster, and Stratified.
What is the first step in sampling? ›The first stage in the sampling process is to clearly define target population. Population is commonly related to the number of people living in a particular country, or in particular, a group or number of elements that researcher plans to study among.
What are the 4 steps in sampling distribution? ›To create a sampling distribution, research must (1) select a random sample of a specific size (N) from a population, (2) calculate the chosen statistic for this sample (e.g., mean), (3) plot this statistic on a frequency distribution, and (4) repeat these steps an infinite number of times.
What is meant by 3 stage of sampling process? ›In a three-stage sampling without replacement design, a sample of primary units is selected, then a sample of secondary units is chosen from each of the selected primary units, and finally a sample of tertiary units is chosen from each selected secondary unit on day K.
What are the 6 steps in random sampling? ›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.
What is 5 simple random sampling is a sampling method? ›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.
How many types of sampling are there? ›There are two main types of sampling: probability sampling and non-probability sampling. The main difference between the two types of sampling is how the sample is selected from the population.
What is sampling with example? ›For example, a random sample may include choosing the names of 25 employees out of a hat in a company of 250 employees. The population is all 250 employees, and the sample is random because each employee has an equal chance of being chosen.
What are the main methods of sampling? ›Probability Sampling methods are further classified into different types, such as simple random sampling, systematic sampling, stratified sampling, and clustered sampling.
Which 7 steps are involved in the sampling process? ›
- Identify the Target population (Population of interest) Target population refers to the group of individuals or objects to which researchers are interested in generalizing their findings. ...
- Select a sampling frame. ...
- Specify the sampling technique. ...
- Determine the sample size. ...
- Execute the sampling plan.
In general, three or four factors must be known or estimated to calculate sample size: (1) the effect size (usually the difference between 2 groups); (2) the population standard deviation (for continuous data); (3) the desired power of the experiment to detect the postulated effect; and (4) the significance level.
What are 4 examples of sampling technique? ›- Simple random sampling. ...
- Systematic sampling. ...
- Stratified sampling. ...
- Clustered sampling. ...
- Convenience sampling. ...
- Quota sampling. ...
- Judgement (or Purposive) Sampling. ...
- Snowball sampling.