The difference between population and sample is that the population includes all the units from a set of data. The sample includes a small group of units selected from the population For example, a population may be all people living in Australia and the sample may be a specific group of people living in Australia. Another example could be that you want to check the number of people nearing retirement age in an organization.
Your population is the entire workforce of the organization, whereas your sample could be the employees who are older than 50 years old. When we read the term population we think of the people living in a country. However, when carrying out data analysis and comparing a set of data statistically the word population has a different meaning. A population includes all members of a specific group of data.
For example, the mean age of women. This is a hypothetical population because it includes all women who have lived, are alive and will live in the future. It is humanly impossible to test the entire population in the above scenario because not all members of the population are observable for e.
Even if it is possible to test the entire population it will incur huge costs and a lot of time. Instead, we could use a subset of the population that is a sample. The sample helps to carry out a test on the above population and find the mean age of women.
Say you are testing the effect of a new fertilizer on crop yield. All the crop fields represent your population, whereas the 10 crop fields you tested correspond to your sample. Since a sample is a subset of a population, a sample is always smaller than the population.
It might be the case that you study such a narrow population e. As mentioned at the beginning of this article, one of the main concern in statistics is being able to draw conclusions about a population from a representative sample. Why using a sample of the population and not directly the population?
In general it is almost always impossible to carry out measurements for the entire study population because:. For these reasons, measurements are made on a subgroup of observations from the population, i. These measures are then used to draw conclusions about the population of interest.
With an appropriate methodology and a sufficiently large sample size, the results obtained on a sample are often almost as accurate as those that would be obtained on the entire population. Of course, the sample must be selected to be representative of the population under study. If participants are included in a study on a voluntary basis, there is a serious concern that the resulting sample may not be representative of the population.
It may be the case that volunteers are different in terms of the parameter 2 of interest, leading to a selection bias. It might be the case that people having access to internet have different wages than people who do not have access. The gold standard to select a sample representative of the population under study is by selecting a random sample. A random sample is a sample selected at random from the population so that each member of the population has an equal chance of being selected.
A random sample is usually an unbiased sample, that is, a sample whose randomness is not in doubt. Although Population and Sample are two different terms, they both are related to each other. The population is used to draw samples. To make statistical inferences about the population is the primary purpose of the sample. The better the quality of the sample, the higher the level of accuracy of generalization.
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Population vs Sample — All you need to know. Population vs Sample — the difference The concept of population vs sample is an important one, for every researcher to comprehend.
What is a sample in market research? Here are the most common sampling techniques: Sampling techniques are broadly classified as two types: Probability sampling and non-probability sampling. Probability sampling — Samples chosen based on the theory of probability. Simple random sampling b.
Cluster sampling c. Systematic sampling d. Convenience sampling b. Judgemental or Purposive sampling c. Snowball sampling d. Quota sampling How to choose high-quality samples: Although we make sure that all the members of a population have an equal chance to be included in the sample, it does not mean that the samples derived from a particular population and satisfying the criterion will be alike.
Population vs Sample — top seven reasons to choose a sample from a given population Sampling is a must to conduct any research study. Here are the top seven reasons to use a sample: Practicality: In most cases, a population can be too large to collect accurate data — which is not practical. An example of a population would be the entire student body at a school. It would contain all the students who study in that school at the time of data collection.
Depending on the problem statement, data from each of these students is collected. An example is the students who speak Hindi among the students of a school. For the above situation, it is easy to collect data. The population is small and willing to provide data and can be contacted. The data collected will be complete and reliable. If you had to collect the same data from a larger population, say the entire country of India, it would be impossible to draw reliable conclusions because of geographical and accessibility constraints, not to mention time and resource constraints.
A lot of data would be missing or might be unreliable. Furthermore, due to accessibility issues, marginalized tribes or villages might not provide data at all, making the data biased towards certain regions or groups.
A sample represents the group of interest from the population, which you will use to represent the data. The sample is an unbiased subset of the population that best represents the whole data.
To overcome the restraints of a population, you can sometimes collect data from a subset of your population and then consider it as the general norm. You collect the subset information from the groups who have taken part in the study, making the data reliable. The results obtained for different groups who took part in the study can be extrapolated to generalize for the population.
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