Population vs. Sample — Tutorial & Examples

In the realm of research, the terms ‘population’ and ‘sample’ frequently surface, often leading many students to mistakenly view them as interchangeable. Yet, their distinct roles and meanings are foundational to research methodology. This article delves into the nuances that distinguish ‘population’ from ‘sample’.

Definition: Population vs. sample

A population refers to an entire group of elements or people about which you aim to draw conclusions in research. On the other hand, a sample refers to a particular selection of the population from which you will collect data. This leads to a sample mostly being smaller than a population.

The differences

The following table will give you an overview about what differentiates a population from a sample.

Population vs. sample: Collecting data

There are many differences between a population and a sample that you need to understand. Besides the definition, you may also benefit from knowing population vs. sample differences in the context of data collection.

Collect data from a population

In research, you use a population when your research question requires you to have data from all members of a population. Collecting data from a small, easily accessible, and cooperative population is relatively easy. However, the challenge usually comes when dealing with larger and dispersed populations, making it difficult and nearly impossible to gather information from each member of the population.

It is also challenging to contact, locate, and encourage data collection participation from marginalized and low-income groups. For this reason, population counts may end up being incomplete and biased towards specific groups. Therefore, researchers lean more towards sampling for more precise inferences regarding a population and bias prevention.

Collect data from a sample

A sample is typically beneficial, when you have a large, geographically disseminated, or hard-to-contact population. In research, you can utilize data from a sample through statistical analysis to estimate or test hypotheses regarding a population.

Population

Types of Population

If you are considering to use a whole population for your research, it is necessary to be aware of the type of population you are focusing on. Not every population can be used as a research subject, as some of them are just impossible to cover.

• Finite populations are those, where you can count and identify each single member. This could for example be the employees of a company or the students of a school.
• An infinite population, on the other hand, is impossible to count as it is too big for this. An example would be the entire human population on earth.
• Closed populations do not allow new members to join, meaning their size will not change over time. For example, all sixth graders in a school that advanced to the next grade. Closed populations are precisely defined on a certain date that does not allow anyone, who might meet the qualifications later on, to join in.
• Open populations cannot be measured precisely, as new members can join at any time, for example the inhabitants of a country or students at a university.

Disadvantages

Even though population studies have plenty of benefits, there is a reason why participants for studies are mostly samples. The following disadvantages will give you an idea why this is the case.

• Expensive: Collecting data from a larger population is time and cost expensive in both collection and analysis.
• Access: Not every person in a group wants to spend time on a survey, so it is difficult to get a complete data set, even if you manage to reach out to ever single member of that population.
• Too much information: The large sample size makes it difficult to assess each member individually. This leads to a lack or overflow of information that may not always fit your research goals anymore.

Advantages

Despite how time and work expensive it may be to conduct research with a whole population, there are quite a few good reasons on why you should still do it.

• Accuracy and representativeness: As the entire population is covered in the study, the results are highly accurate and obviously represent the group perfectly.
• Statistical power: The large sample size leads to an immense statistical power of the study, as the risk of excluding anyone’s opinion is very low.
• Rare events: A whole population often includes minor opinions and events that can lead to a shift in results later on. Those are frequently neglected in sample studies, while a survey about the entire population may be able to cover those.
• Subgroup analysis: Studying an entire group allows searching for differences between different subunits in it. This leads to more precise results and analysis.

Sampling

Types of sampling

If you decide on sampling participants for your research, the next step is to decide on the right method among those common sampling techniques. Furthermore, you need to be aware of sampling bias, as you select your samples in order to keep your study valid and representative.

• In probability sampling, the subjects are chosen randomly, reducing the risk of introducing bias to the study.
  • In random sampling, participants are selected by drawing lots or similar techniques to make sure no one can influence the sampling process.
  • Systematic sampling chooses participants by a system, for example every fourth person from a list in alphabetical order.
  • Stratified sampling divides the population into different subgroups, according to factors like age, gender or wealth, first and then chooses participants from those.
  • Cluster sampling also selects certain subgroups first, only that these clusters are completely random.
• Non-probability sampling methods are prone to bias, as samples are chosen non-randomly, being selected by the researcher.
  • Convenience sampling happens, when subjects are chosen among the people close to the researcher or from those that are being easily approachable.
  • Judgmental sampling refers to the action of selecting samples based on certain criteria the researcher deems relevant. He chooses the participants according to his own impression of them, which can be highly subjective.
  • Quota sampling tries to acknowledge the demographic characteristics of the population, selecting participants accordingly. This can refer to age, gender, wealth, social status, etc.

Advantages

Sampling is one of the most common ways to gain information about people in research. As such, it has of course a great number of advantages, which will be described below.

• Cost-effective: Sampling needs a lot less time and resources than conducting research with a whole population.
• High quality: As there are fewer individuals to focus on, the questions can be more precise and detailed, even extended with follow-ups to acquire complete and accurate data sets.
• Easier to analyze: As the number of samples is smaller and the questions more focused, it is easier and quicker to analyze the data.
• Feasibility: Conducting research, especially as a university student, can be a rather difficult task. Sampling participants is a great option to conduct your own study.

Disadvantages

The biggest problem with sampling is, of course, representativeness, but there are other disadvantages too, which should be considered before conducting your research.

• Sampling bias: If the samples are not sampled correctly, sampling bias will affect your study and its validity.
• Generalizability: The smaller scope of samples does not always represent the entire population accurately, leading to a lack in generalizability and of the research.
• Statistical power: A smaller number of samples always creates a loss in statistical power, as critics can easily accuse the researcher of bias or sampling errors if they deem the study not representative.

Parameter vs. Statistic

When you collect data from your study, you usually transfer the results of the statistics from your samples to the parameter of the population. The parameter is the measure of the variable in the entire population you are trying to approximate with your study. However, if your sampling was not representative, a sampling error occurs, leading you to wrong conclusions. To lower the impact of this sampling error, it is always best to make the sample group as large as possible to increase the chance of representing every opinion.