Sampling

Sampling is a technique used to make observations and conclusions about a population.

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.

Advantages and disadvantages of a census and a 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.

What are the different types of sampling?

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.

Random sampling

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.

• 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.

• 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.

Non-random sampling

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.

• 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.

Different types of data

• 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.