Sampling Bias and How to Avoid It | Types & Examples

What is Sampling?

Sampling is the process of selecting a subset of individuals or objects from a larger group or population, in order to gather information about the group as a whole. This technique is commonly used in research, surveys, and other forms of data collection to save time, money, and resources that would be required to collect information from every member of a population.

So, a sampling method is a procedure for selecting sample elements from a population. Sampling is necessary to make inferences about a population. If sample is not representative it is biased — you cannot generalize to the population from your statistical data.

Representative Sample

A subset of the population from which data are collected that accurately reflects the population

Bias

The systematic favoring of certain outcomes

Sampling Bias

Sampling bias occurs when the selection of individuals or objects to be included in a sample is not random or representative of the population being studied. This can lead to an overrepresentation or underrepresentation of certain groups or characteristics within the sample, which can in turn affect the accuracy and generalizability of the study’s results.

Sampling Bias Example – Political Poll

An example of sampling bias could be a political poll that only surveys individuals who are members of a particular political party. The results of the poll may be biased towards the views and opinions of that particular party, and may not accurately reflect the views and opinions of the broader population. This bias could occur if the pollster only surveyed individuals who had volunteered to participate, or if they had used a convenience sampling technique such as surveying individuals at a political rally or event.

Sampling Bias Example – Study of the Health Outcomes

Another example could be a study of the health outcomes of a particular medication that only includes participants who have self-selected to take the medication. This sample may not be representative of the broader population of individuals who are prescribed the medication, as those who self-select to take it may have different health conditions, risk factors, or treatment preferences than those who do not. This could lead to biased estimates of the medication’s effectiveness and safety.

In both of these examples, the sample selection process is not random or representative of the population being studied, and this can lead to biased or inaccurate results.

Causes of sampling bias

Sampling bias occurs when a sample is not representative of the population from which it is drawn. Here are some common causes of sampling bias:

➣Selection bias

Selection bias occurs when the sample is not randomly selected from the population, but instead, certain individuals or groups are intentionally or unintentionally overrepresented or underrepresented in the sample.

Selection Bias Example

Suppose a researcher is interested in studying the average income of individuals in a certain city. The researcher decides to use a phone book to select participants for the study. However, the phone book only includes landline phone numbers, which tend to be used by older and more affluent individuals. This would result in a sample that is biased towards older and wealthier individuals, and would not accurately represent the income distribution of the entire population.

Another example is in clinical trials, where participants are typically recruited through advertisements or referrals from doctors. If the ads or referrals only reach a certain segment of the population, such as those who have access to certain health care services, it can lead to a biased sample.

In both of these examples, the selection process is not random, and certain groups are overrepresented or underrepresented in the sample, leading to a biased result that may not be generalizable to the entire population.

➣Volunteer Bias

Volunteer Bias occurs when individuals who choose to participate in a study have different characteristics than those who do not volunteer, this leads to volunteer bias.

Volunteer Bias Example

Suppose a researcher is interested in studying the health habits of individuals in a particular community. The researcher decides to recruit participants through advertisements on social media platforms and in local newspapers. However, individuals who choose to respond to these ads may be more health-conscious than the general population. They may be more likely to exercise regularly, eat a healthy diet, and avoid unhealthy behaviors such as smoking and excessive alcohol consumption.

As a result, the sample of participants in the study would be biased towards individuals who have healthier habits than the general population. This can lead to inaccurate conclusions about the prevalence of certain health behaviors in the community, and limit the generalizability of the study’s findings.

Another example could be a survey about political opinions that is conducted through an online platform or social media. People who choose to respond to the survey may have stronger political views and be more interested in politics than those who do not respond, leading to a biased sample.

In both of these examples, the bias occurs because the sample is made up of volunteers who may have different characteristics than the population as a whole.

➣Non-response Bias

Non-response Bias occurs when the individuals who do not respond to a survey or study have different characteristics than those who do respond. This can lead to inaccurate or incomplete results, as the responses of those who do not participate may differ from those who do.

Non-response Bias Example

Suppose a researcher is interested in studying the opinions of voters in a particular district about a local election. The researcher sends a survey to all registered voters in the district, but only receives responses from 30% of them. The respondents may be more likely to have strong opinions about the election or be more politically engaged than those who did not respond.

As a result, the sample of respondents in the study may not accurately represent the opinions of all registered voters in the district. The opinions of those who did not respond, such as those who are less politically engaged or have weaker opinions, are not captured in the study, leading to non-response bias.

Another example could be a survey conducted on a sensitive topic, such as drug use or mental health. Individuals who choose not to respond to the survey may be less likely to report their experiences or opinions on the topic, leading to a biased sample.

In both of these examples, the bias occurs because the non-respondents have different characteristics than the respondents, leading to a sample that is not representative of the population.

➣Measurement bias

Measurement bias happens when the measurement instrument used to collect data is flawed, leading to inaccurate or incomplete data. This can lead to inaccurate or incomplete results, as the data may not reflect the true underlying phenomenon being studied.

There are many different types of measurement bias, including:

• Instrument bias: This occurs when the measuring instrument is faulty, leading to incorrect or inconsistent measurements. For example, a bathroom scale that consistently overestimates weight would introduce instrument bias into any study that relies on weight measurements.
• Observer bias: This occurs when the person collecting the data has preconceived ideas or expectations that influence their observations. For example, if a researcher believes that men are more aggressive than women, they may be more likely to interpret a male participant’s behavior as aggressive, even if it is not.
• Social desirability bias: This occurs when participants in a study provide responses that they believe are socially desirable, rather than their true beliefs or experiences. For example, a participant in a survey on drug use may underreport their drug use to avoid being stigmatized.
• Recall bias: This occurs when participants have difficulty accurately recalling past events or experiences. For example, a participant in a study on childhood abuse may not accurately remember the frequency or severity of the abuse they experienced.

Measurement Bias Example

An example of measurement bias could occur in a study examining the effectiveness of a new medication for a particular medical condition. If the study relies on self-reported symptoms from participants, there may be measurement bias if some participants tend to over-report or under-report their symptoms.

For instance, participants who believe that the medication will help them may over-report improvements in their symptoms, even if they do not experience significant changes. Conversely, participants who are skeptical about the medication may under-report their symptoms, even if they do experience improvements.

This could lead to biased results, as the data may not accurately reflect the true effectiveness of the medication.

➣Time Interval Bias

This type of bias occurs when a study is conducted over a limited period and does not consider variations over time. This can happen when the outcome of interest changes over time, and the length of the interval used to measure it affects the observed rates or outcomes.

Time Interval Bias Example

An example of time interval bias could occur in a study examining the survival rates of cancer patients. If the study only considers patients who survived for a certain period, such as one year, it may overestimate the overall survival rate. This is because patients who die shortly after the one-year mark will not be included in the analysis, leading to an overestimate of the overall survival rate.

Conversely, if the study only considers patients who survived for a longer period, such as five years, it may underestimate the overall survival rate. This is because patients who die before the five-year mark will not be included in the analysis, leading to an underestimate of the overall survival rate.

To minimize time interval bias in this case, the study could use a longer follow-up period to capture more deaths and improve the accuracy of the survival rate estimate. Alternatively, the study could use statistical methods to account for the length of follow-up time when estimating the survival rate.

➣Survivorship Bias

This occurs when the study only includes individuals who have survived a particular event or condition, leading to a biased sample that may not accurately represent the population as a whole. This can lead to overestimating the success rate or effectiveness of the process or event, as the outcomes of those that were lost or excluded are not included in the analysis.

Survivorship Bias Example

An example of survivorship bias could occur in a study on the success of entrepreneurs. If the study only considers successful entrepreneurs who have made it to the top of their industry, it may lead to an overestimation of the factors that contribute to success. This is because the study only considers those who have survived the competitive landscape of entrepreneurship and excludes those who failed or dropped out.

Similarly, survivorship bias can occur in investment analysis when evaluating the performance of a particular investment or asset. If we only look at the returns of the assets that have survived, we may underestimate the risk associated with that investment, as we have excluded the assets that did not survive.

To minimize survivorship bias, it is important to include information about the individuals or items that were lost or excluded from the analysis. For example, in the study on entrepreneurship, the study could also consider factors that contribute to failure, or in the investment analysis, the analysis could include information on the assets that were lost.

➣Hawthorne Effect

When individuals modify their behavior because they know they are being observed, this leads to the Hawthorne effect, which can create biased data. This effect can impact the results of a study, as the observed changes in behavior or performance may not accurately reflect the participant’s true behavior or performance in the absence of observation.

Hawthorne Effect Example

An example of the Hawthorne Effect could occur in a study on worker productivity in a factory. If workers know they are being observed or studied, they may increase their productivity to impress the researchers, regardless of any actual changes in their work habits. This may lead to an overestimation of the effectiveness of the intervention or treatment being studied.

Similarly, the Hawthorne Effect can occur in healthcare settings. If patients know they are being observed or monitored, they may change their behavior or compliance with treatment regimens, regardless of any actual changes in their health outcomes. This may lead to an overestimation of the effectiveness of a treatment or intervention.

To minimize the Hawthorne Effect in studies, researchers can use a variety of methods, such as blinding participants to the purpose of the study, conducting observations without the knowledge of participants, or using control groups to compare changes in behavior or performance between observed and unobserved groups.

How to avoid or correct sampling bias

Sampling bias is a type of bias that occurs when the sample used in a study is not representative of the population of interest, leading to inaccurate or misleading results. Here are some ways to avoid or correct sampling bias:

• Use random sampling: Random sampling ensures that each member of the population has an equal chance of being selected for the study. This reduces the likelihood of bias and ensures that the sample is representative of the population.
• Increase sample size: A larger sample size reduces the impact of sampling bias on the study results. A larger sample size is also more likely to be representative of the population.
• Stratified sampling: Stratified sampling is a technique that ensures that the sample includes members from different subgroups of the population. This reduces the likelihood of bias in the sample and ensures that the sample is representative of the population.
• Oversampling: Oversampling is a technique that involves deliberately over-representing a particular subgroup of the population in the sample. This is useful when the subgroup is of particular interest or when the subgroup is underrepresented in the population.
• Weighting: Weighting is a technique that assigns more weight to certain observations in the analysis to correct for any imbalances in the sample. This is useful when the sample is not representative of the population.
• Sensitivity analysis: Sensitivity analysis involves testing the robustness of the study results by varying the assumptions or parameters used in the analysis. This can help identify and correct any biases in the study results.

By employing one or more of these techniques, researchers can minimize the impact of sampling bias on their study results.

Oversampling to avoid bias

Oversampling is a technique that involves deliberately over-representing a particular subgroup of the population in the sample to reduce bias. This is useful when the subgroup is of particular interest or when the subgroup is underrepresented in the population.

For example, if a study is investigating the health outcomes of a rare disease, the sample size may need to be increased to ensure there are enough participants with the disease. Without oversampling, the small number of participants with the disease may not be representative of the population, leading to biased results