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Sampling is a technique used to make observations and conclusions about a population.
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There are some important statistical terms that you need to bear in mind:
A population is the group that we want to observe and find out more about.
A census is used to observe every member of the population.
A sample can be used to measure a smaller amount of the population, which is then used to infer information about the population as a whole.
A sampling unit is the person or thing that is being observed.
A sampling frame is the list of all the people or things within the population that can be observed.
It is important to consider how the sample you use can affect the conclusions that you make from the research, as different samples may lead to different conclusions. When using a larger sample, you are more likely to get a more accurate inference about the whole population. However, this can be difficult and require more resources. If you are researching a very varied population it may be more beneficial to use a larger sample.
Although both censuses and samples are helpful when finding out information about a population, they both have advantages and disadvantages.
Census advantages:
It gives you an accurate result as it obverses the whole population.
Census disadvantages:
Time-consuming
Costly
Difficult to process when there is a large amount of data.
Sample advantages:
Less time-consuming as fewer people have to respond.
Fewer data to process.
Sample disadvantages:
As a sample is smaller than a census, the data may not be as representative as in a census.
There are many different types of sampling techniques that can be used to find out information about a population. The two main types of sampling techniques are random and non-random. Within these types of sampling techniques, there are also different sampling methods.
There are three different methods of random sampling: simple random sampling, systematic sampling, and stratified sampling. Many people prefer random sampling as everyone within the population has an equal chance of being picked for the sample. This reduces the bias.
Simple random sampling: This can be done by using a sampling frame. The list of people within the population is then numbered and a selection of the numbers is chosen randomly. To do this, you could use a calculator or a number generator. One advantage of this sampling method is that there is no bias as each sampling unit has an equal chance of being selected for research. However, you need a sample frame and it is difficult to use this method if the sample size is large.
A researcher numbers all the people within a school to find out what they think of their classes, numbers are then chosen at random to create the sample.
Systematic sampling: This method involves choosing the sample following a simple rule, such as regular intervals from an ordered list. An advantage of this method is that it is easy to use, especially with larger samples. However, you need a sample frame, which could cause bias if you use a frame that is not random.
The researcher may choose systematic sampling and decide to use a pattern of three, so every third sample unit on the list would be chosen as part of the sample.
Stratified sampling: For this method, the population is split into smaller groups based on behaviours and characteristics, for example, males and females. Then, you take a random selection from each subgroup, known as strata. This sampling method is able to accurately represent the population and makes sure that each group within the population is represented. However, it can be difficult to place the population within each group.
The researcher knows that the population contains a range of age groups, they will randomly choose sample units from each strata to make up the sample.
There are two different methods for non-random sampling: quota sampling and opportunity sampling.
Quota sampling: This method involves splitting the sample into groups based on characteristics, whilst interviewing them. Once there are enough sample units per group, the researcher stops taking any further data. There are many advantages of this sampling method: it doesn't require a sampling frame, it's quick and easy to do, and it allows the researcher to make comparisons between the groups within the population. However, there is a chance of some bias and the division of the groups may not be accurate.
The researcher is looking to find out a populations opinion on their latest product, they know the population is 40% male and 60% female, therefore the sample will also represent this, they want to gather a sample of 100 sample units and once they have found 40 males and 60 females their quota will be filled.
Opportunity sampling: This is when the researcher takes a sample from people that are able to complete the research at the time it is being completed, as well as fitting the sample criteria. While this sampling method is easy to do and is low in cost, it is unlikely that you will get an accurate representation of the population.
When using opportunity sampling the researcher may ask the first ten people they see on their way to work whilst standing in a high street.
There are different types of data that can be collected from sampling, they are;
Qualitative - This is descriptive data, for example, your hair colour.
Quantitative - This is numerical data, for example, your height.
Continuous - This is data that can take any integer and non-integer within a given range, for example, the temperature.
Discrete - This is data that can only take a specific non-decimal number, for example, the number of children in a class.
Sampling is a technique used to make observations and inferences about a population.
There are two different sampling techniques: random and non-random.
There are three different methods of random sampling: simple random sampling, systematic sampling, and stratified sampling.
There are two methods of non-random sampling: quota sampling and opportunity sampling.
Each method can be critiqued and has its own advantages and disadvantages.
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What is sampling?
Sampling is a technique used to make observations and conclusions about a population.
What is random sampling?
Random sampling is a sampling method where you randomly select sample units from the population to be part of a sample.
What is stratified sampling?
Stratified sampling is a type of random sampling where you split the population into smaller groups based on characteristics, and then randomly choose sample units from each group.
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