Sampling bias occurs when the sample collected for a study systematically differs from the target population. Below are some common types of sampling bias:
- Self-selection bias: People who choose to participate in a study differ from the general population in an important way (e.g., motivation, interest).
- Nonresponse bias: Those who are unable or unwilling to respond often share key characteristics, and their absence may skew results.
- Healthy user bias: Individuals who are able or willing to participate are often healthier or more health-conscious than nonparticipants.
- Survivorship bias: Data are only available for individuals or outcomes that pass a certain filter (e.g., those who survive an event); those that didn’t are ignored.
- Undercoverage bias: Certain subgroups are systematically excluded from the sample, leading to skewed representation.
- Prescreening bias: Eligibility criteria (e.g., age, language) may unintentionally exclude relevant parts of the population.
Not sure which types of sampling bias are applicable to your study? AI tools are a great way to generate ideas and receive dynamic feedback on study design. Try QuillBot’s free AI Chat the next time you’re feeling short on inspiration.
Read this FAQ: What are some types of sampling bias?
There’s not a universally agreed-upon distinction between sampling bias and selection bias, but sampling bias is often considered a subtype of selection bias.
Sampling bias occurs when a sample is not random (i.e., it differs from the target population). It impacts external validity—how well the results generalize from the sample to the population.
Selection bias, on the other hand, refers more broadly to bias introduced when selecting who to include in a study. It impacts internal validity—whether your results can be explained by the independent variable you manipulated (and not by other confounds).
The distinction between sampling and selection bias is complex. AI tools like QuillBot’s Paraphrasing Tool can be helpful when trying to parse difficult concepts.
Read this FAQ: What is the difference between sampling bias and selection bias?
Purposive sampling is a sampling method where the researcher intentionally selects individuals to study based on desired characteristics or experiences relevant to their research question.
There are several common approaches to purposive sampling:
- Maximum variation (heterogeneous) sampling: includes individuals who differ from each other as much as possible to capture a range of experiences
- Homogeneous sampling: includes individuals who are very similar to each other to enable a detailed exploration of a certain subgroup
- Typical case sampling: includes individuals who best reflect the average or norm of a population
- Extreme (deviant) case sampling: includes outliers who fall significantly above or below the norm
- Critical case sampling: includes individuals whose results are likely to generalize—if it happens to them, it would probably happen to anyone
Expert sampling: includes individuals with specialized knowledge or expertise relevant to the research topic
Read this FAQ: What are the types of purposive sampling?
Random sampling—or probability sampling—includes a range of sampling methods used to select a subgroup of individuals from a larger population. A key characteristic of random sampling is that every individual has a known chance of being selected.
Purposive sampling, on the other hand, is a non-probability sampling technique. In this method, not every individual has a known chance of being included in the sample. Instead, the researcher chooses who they include in their sample based on certain traits or experiences. This can be helpful if the researcher is very familiar with the population they are studying and wants to gain rich, targeted insight rather than generalizable information.
Purposive sampling is often easier and more efficient than random sampling. However, purposive sampling is highly susceptible to sampling bias. Random sampling is a better approach for obtaining a representative sample that reflects the broader population.
To learn more about random sampling vs purposive sampling, use QuillBot’s free AI search.
Read this FAQ: What is the difference between random sampling and purposive sampling?
Purposive sampling and convenience sampling are two non-probability sampling methods, meaning not every individual from the population has an equal chance of being selected. Sampling methods are ways of choosing individuals from a population to study.
Purposive sampling is when a researcher hand-picks individuals because they possess specific traits or characteristics. For example, someone studying successful teaching techniques might only include teachers who have recently won awards in their sample.
On the other hand, convenience sampling involves selecting individuals simply because they are easily accessible. For example, a business might ask their social media followers to complete a survey.
Convenience sampling and purposive sampling are not mutually exclusive—a researcher might use some combination of both techniques when obtaining a sample for their study.
Both of these techniques are susceptible to sampling bias. Individuals who are readily accessible or who the researcher chooses to participate may not be fully representative of the broader population.
Read this FAQ: What is the difference between purposive sampling and convenience sampling?
Purposive sampling, or judgment sampling, is a non-probability sampling method that involves hand-picking individuals to include in a study based on certain characteristics.
For example, a researcher studying how cancer patients cope with terminal illness may directly recruit several late-stage cancer patients who are receiving palliative care at their clinic.
Unlike in probability sampling, not every cancer patient has an equal chance of being selected. Instead, the researcher can choose the cases they feel will be most informative.
Purposive sampling can be helpful when the researcher is very familiar with the population they are studying, as it allows them to select individuals who best represent this group. However, any biases the researcher holds may be reflected in the sample.
Read this FAQ: What is an example of purposive sampling?
Knowing whether your data are from a population or a sample is key to properly analyzing or interpreting your results.
If your data are from a subset of the group you are studying, your data represent a sample. If instead your data have been collected from every single individual you are interested in studying, your data are from a population.
If you are analyzing data you did not collect yourself, consider how likely it is that the researchers who collected this data gathered measurements from every single individual they were interested in studying.
Researchers generally collect data from a smaller group and use the results to make inferences about the population, so there’s a good chance that these data are from a sample rather than a population.
Read this FAQ: How do I know if my data are from a population or a sample?
In statistics, population parameters are characteristics that describe a population (such as mean, standard deviation, and variance). They are calculated using the data from every member of the group you want to learn about, so they provide a completely accurate description of that population.
Sample statistics, on the other hand, are calculated from a sample (a subset of the population). Sample statistics provide an estimate of population parameters, but because they do not include data from every member of the population, they may be biased or inaccurate.
Read this FAQ: What are sample statistics vs population parameters?
Sampling bias occurs when some individuals in the population are more likely to be included in a sample than others. This can limit how well results generalize to the broader population.
Sampling methods like probability sampling help reduce sampling bias because every individual in the population has a known, non-zero chance of being included in the sample. However, it’s difficult to eliminate sampling bias entirely, so results from a sample should always be interpreted with caution.
Read this FAQ: What is sampling bias?
Simple random sampling is a probability sampling method. Individuals are selected randomly from a list of all members of the population (the sampling frame), so everyone has an equal chance of being included in the sample..
This method has reduced sampling bias compared to other sampling methods, but it can be more difficult to conduct. It requires a complete list of the population and does not consider how easy or difficult it is to reach selected individuals.
Read this FAQ: Is simple random sampling probability or nonprobability sampling?
