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A sample is a subset (smaller group) of a population or target population. Every study has an inquiry and in order to answer it, a sample of a population is taken and studied. The sample is meant to be representative of the population and is meant to derive insights on the population as a whole. Samples need to be used because oftentimes it is extremely difficult, or impossible in some cases, to study a whole population.
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In research, population is defined as a whole set of elements that all qualify a standard parameter. In research, “population” doesn’t necessarily refer to the human population, instead it refers to any parameter of data that possesses a common trait.
For example, it can be the total number of buildings in a city or the total number of shoe shops in an area.
In research, a sample is defined as a subset of a population. This sample is meant to be generalizable to the population in a study so that researchers can make inferences on the behavior or characteristics of the whole population.
For example, if a study is aiming to understand the sugar consumption of American teenagers, only a sample of American teenagers will be studied rather than the whole American teenage population.
There are many different sampling techniques and researchers choose the one that is most suited to their study. This will depend on a number of factors such as the type of study, financial limitations, time limitations etc.
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Sampling techniques can be broadly classified into two categories:
This sampling technique chooses a sample based on the theory of probability, giving all elements of a target population an equal chance of being selected in the sample group. These are the main types of probability sampling techniques:
This sampling technique relies on researcher judgment or convenience in order to choose a sample group. These are the main types of non probability sampling techniques:
The measure that describes the whole population is known as a parameter. The measure that describes the sample is known as a statistic.
Hypothesis testing is used to estimate how much a sample statistic differs from the population parameter, and the difference between the two is known as “sampling error”. Sampling errors exist because no sample will be identical to the population.
The lower the sampling error is, the better, as researchers want the study’s findings to be generalizable to the whole population. An easy way of reducing sampling error is by increasing the sample size.
The following table outlines some key differences between population and sample:
A whole set of elements that all qualify a standard parameter.
A subset of a population.
Surveys of whole populations do not have a margin of error, barring human inaccuracy.
Surveys of sample groups hold accurate results only after taking the margin of error into consideration.
The measurable characteristic of a population such as its standard deviation or mean is known as ‘parameter’. It is the measurable or numeric element that defines the system of a set.
The measurable characteristic of a sample is known as a “statistic”. It is the descriptive component of the sample that is found using sample proportion or sample mean.
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