No matter what kind of research you are doing, and irrespective of the discipline you are studying, collecting and analyzing data correctly is key to ensuring that your findings are reliable. However, collecting data for research can be challenging and most people find themselves having to decide whether to collect data from an entire population or to use a smaller sample.
That is why understanding the differences between population parameters and sample statistics and knowing when to use each is an essential skill – required for drawing valid conclusions and ensuring that research outcomes are accurate, and relevant. This is important because inaccurate data can lead to wrong conclusions and, even worse, can damage the credibility of your research, wasting both time and resources.
Before we go further, let us try and understand what the terms population and sample mean from a research perspective.
Defining population from a data perspective
The word ‘population’ is usually used to describe a community, or a particular group or type of people living in a place. In scientific research however, the term does not necessarily refer to people alone – it can be used to describe any group that meets a set criterion for a particular study – from objects, organizations, animals, to even events that share a common characteristic. For example, if you are conducting a study on the effects of work-related stress at hospitals, the population here would refer to all those working at hospitals in varying capacities.
Defining sample from a data perspective
A sample, on the other hand, is a smaller group selected from a larger population. Think of it as a snapshot of the whole, one that represents the larger population, and is much easier to manage and study. Researchers must ideally, make random selections to avoid any unconscious bias and ensure accuracy. Taking the previous example, you can probably study 500 doctors and nurses from various hospitals in different states. This sample selection will allow you to make inferences on work-related stress across hospitals without collecting data from every individual.
The importance of sampling
- Sampling is invaluable in research studies, particularly when you are working with large populations. Conducting research on an entire population can be tedious and impractical as it may take years, depending on the size. Sampling, on the other hand, allows researchers to complete their studies more efficiently and within a shorter timeframe.
- Sometimes it may just not be possible to access or study the entire population – due to geographic constraints, legal issues, socio-cultural or language barriers, and in cases like these using a sample becomes the only feasible option.
- Moreover, gathering data from a large population can prove expensive. In situation with budgetary constraints, sampling can be used to reduce costs while still ensuring the data is representative and accurate. By ensuring that the sample has been chosen after careful consideration, researchers can use statistical methods to make accurate estimates and inferences regarding larger populations.
Key differences between sampling and population data
Let us take a quick look at the difference between population parameters and sampling statistics –
| Population Data | Sampling Data |
| Denotes a study of all individuals or items in a group that is relevant to the study. | This represents a subset of the entire population that has been selected for the study. |
| Involves gathering data from every individual or unit. | Involves randomly selecting a smaller group, and gathering data from them. |
| Is considered to be complete in all aspects and also more accurate as it studies each member or item of the population. | Since it represents only a fraction, or part of the population, it is considered incomplete and may have sampling errors. |
| This is usually more expensive due to the need to collect data from everyone. | Less expensive, as it only involves collecting data from a smaller portion of the population. |
| Tedious and time-consuming, collecting population data can take a very long time based on the size of the population. | As only a subset of the population is surveyed, sampling data can be collected efficiently and much more quickly. |
How to decide between using population data or sampling data in research?
Early career researchers sometimes struggle to answer this question. So how do you decide? Well, if the population is small, manageable, easily accessible, and well-defined, using population data might be a good idea. It eliminates sampling errors, helps avoid possible biases, and ensures that the results are accurate. This is especially important in studies where precision is key, such as clinical trials or specific case studies.
On the other hand, sampling data can be used when researchers are faced with the task of studying larger populations. However, there is always a chance of some level of error with sampling, which is why researchers must use sample statistics to make valid inferences about the population. Also, reporting potential margins for error when using a sample is a good idea as it provides context for the findings.
Moreover, when collecting sample data, it is imperative to ensure that the sample is representative and accurately reflects the characteristics of the larger population. While techniques such as random sampling or stratified sampling can help minimize bias, do keep in mind the importance of selecting an appropriate sample size – one that will be large enough to provide statistically significant results.
By carefully considering factors such as cost, time, and the size of the population, researchers can select the most appropriate data collection method for their specific needs. Remember, regardless of whether you choose to use population data or a sample statistic, the main aim should always be to ensure that the data is representative, accurate, and capable of supporting valid inferences.
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