Population and sampling
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When surveys are carried out, every single person/object that is relevant cannot be realistically asked/checked. The set of all the people/objects relevant to a survey is called the population. To combat this issue, a sample is taken. A sample is a subset of the population to be used in the survey and a sampling unit is each element of the sample. Sampling is a lot easier to carry out rather than asking every element of the population and is thus less time-consuming and a lot cheaper. Since a sample represents the population it is taken from, it also gives accurate results. Also, in cases of limited resources, sampling is ideal since it doesn't use up the whole population. However, there is still a chance of bias and there are difficulties in selecting a truly representative sample.
The population of a survey is every person/object that is asked/checked during a survey. Using the entire population means that everyone's opinion is accounted for, meaning that the results are free from bias and are more reliable. However, asking the whole population could be extremely time-consuming and costly.
There are many sampling techniques in which samples are collected in different ways. Two of these are random sampling and opportunity sampling. In random sampling, each member of the sample has an equal chance of being selected. Each sampling unit is assigned a number and then numbers are randomly generated to pick out the members to include in a sample. Random sampling is esay to carry out and usually lacks bias, however it can prove to be difficult to get a list of the whole population, expensive, time-consuming and can still introduce bias. Opportunity sampling is the process through which a sample is created by using people from the population that are available at the time and are willing to take part. Opportunity sampling is a lot less time consuming than other sampling techniques and is the easiest method to usw, however the results obtained may not be representative of the full population.
Different surveys have different needs and thus it should be considered which sampling technique will give the best results for that specific survey. In some cases where the representation of the population is of high importance, stratified sampling may be used. What stratified sampling does is it separates the population into different strata depending on different qualities of the population. Then, for each stratum, the exact same process as in random sampling takes place. This is often done to ensure that the sample taken is representative of the population of the survey.
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