In this blog, you’re going to learn all about sampling bias.
This is really important.
Why? Because if you’re not aware of the sampling bias that might creep into your research,
It can make the outcome of your findings unusable.
This is something every researcher on this planet faces and tries to avoid.
If you’re doing any kind of research, understanding sampling bias and knowing how to reduce it will help you achieve strong, reliable outcomes.
You’ll get insights that anyone can trust—and you can publish them anywhere without fear.
1..2..3…Let’s go.
What is Sampling Bias?
Key Points:
Sampling bias occurs when the way participants are chosen for a study favors certain people or groups, resulting in some members of the population having a greater chance of being included than others.
It leads to inaccurate research results because accurate research requires that the samples fully represent the entire Target Population.
Okay, Let me make this relatable:
Imagine you have two jars of cookies. One jar has chocolate cookies (your favorite), and the other has a different flavor.
Your job is to figure out which jar has the best cookies.
But instead of trying cookies equally from both jars, you dive straight into the chocolate cookies and finish the entire jar without even touching the other one.
How can you decide which cookie tastes best if you never gave the second jar a chance?
This is exactly what sampling bias means. Instead of picking samples equally from the population (in this case, the cookie jars), you leaned entirely toward your favorite. By doing this, you’re not getting the full picture.
Who knows? Maybe the other jar has cookies you’d love even more than chocolate.
The same thing happens in research.
If a researcher only collects data from a specific group and ignores the rest, their results won’t reflect the diversity of the whole population.
Without that diversity, the research outcome becomes less accurate and less reliable. So, to find the truth—whether it’s about cookies or research—you’ve got to give everything an equal chance.
Why is sampling bias a threat to research validity and decision-making?
Sampling bias is a problem because it makes research results less reliable.
When a sample doesn’t represent the whole population, the findings can be wrong. This is called “low validity.”
For example, if a study about exercise only includes people who already work out, the results won’t apply to those who don’t exercise.
Not only that:
Biased data can also cause decision-makers to make poor choices.
If a business or researcher uses data that doesn’t reflect every customer’s opinion,
They may end up with wrong information.
For example, if a company surveys only happy customers, they might think all their customers are satisfied.
But this can lead to poor decisions, like not fixing problems that matter to less satisfied customers.
How Does Sampling Bias Occur?
To put it simply, researchers use certain sampling methods to choose the samples from the target population.
And sampling bias happens based on the kind of sampling method the researcher uses.
Here are some typical sampling methods that cause bias:
Recommended Read: Sampling Methods Types and Examples
Types of Sampling Bias
| Type of Sampling Bias | What It Is | Example |
|---|---|---|
| Undercoverage Bias | Happens when some groups in the population are left out or underrepresented. | A national survey done online might miss older adults or those without internet access, leaving their opinions out. |
| Voluntary Response Bias | Occurs when people self-select to join the study, often those with strong feelings about the topic. | A radio station asks for opinions on a topic. Only people with strong views call in, leaving out neutral or indifferent ones. |
| Survivorship Bias | Focuses only on the “surviving” examples, ignoring the ones that failed or didn’t make it. | Studying only successful companies to find what works, ignoring lessons from failed ones. |
| Healthy User Bias | Happens when participants are healthier than the general population, skewing the results. | A diet study with health-conscious volunteers may show better results than if it included less healthy participants. |
| Pre-Screening Bias | When the method of recruiting participants influences who joins, creating a biased sample. | A sleep study advertised in wellness centers attracts health-focused people, missing a broader range of participants. |
| Exclusion Bias | Occurs when certain groups are intentionally or unintentionally left out of the sample. | A health survey excludes low-income groups, missing key issues they face. |
| Berkson’s Fallacy | Happens when studies in specific settings, like hospitals, create false connections between variables. | A hospital study finds obesity and diabetes appear strongly correlated, but this result is due to the biased sample of hospitalized patients. |
Berkson’s Fallacy Example
To explain Berkson’s Fallacy, consider a hypothetical study examining the relationship between obesity and diabetes among patients admitted to a hospital:
Sampling Bias in Historical Research: Lessons from the 1948 U.S. Election
1948 U.S. Election
In the 1948 U.S. presidential election, a famous example of sampling bias occurred.
A telephone survey conducted during the election indicated that Thomas E. Dewey would win by a landslide over Harry S. Truman.
However, this prediction turned out to be incorrect.
The survey failed to take into account that not everyone owned a telephone at the time, particularly lower-middle and lower-class citizens who were more likely to vote for Truman.
This led to an under-representation of a key demographic, resulting in an inaccurate prediction.
The front page of the Chicago Tribune famously ran the incorrect headline, “Dewey Defeats Truman,” based on these flawed survey results.
This incident highlighted how under-coverage bias, or the exclusion of certain groups from the sample, can lead to inaccurate findings.
Sampling Bias vs. Selection Bias: What’s the Difference?
| Concept | Definition | Example | Impact on Results |
|---|---|---|---|
| Selection Bias | Selection bias happens when the respondents included in a study do not represent the larger population from which they were selected. This occurs due to systematic differences between those selected and those not selected. | A clinical trial for a new medication intended for the general population includes participants only from specific hospitals, ignoring broader representation. | Selection bias leads to invalid conclusions that cannot be generalized to the broader population. Only generalizable outcomes are truly representative and reliable. |
| Sampling Bias | Sampling bias happens when the sample collected from the population does not represent the entire population. This occurs due to the method of selection used. | A survey conducted via online platforms includes mainly tech-savvy people, leaving out older adults who may not use online platforms frequently. | Sampling bias results in bad data that misrepresents the true characteristics of the population, causing researchers to draw incorrect conclusions. |
Wait, isn’t both the selection and sampling bias the same?
Selection bias occurs when study participants don’t represent the larger population, affecting internal validity—the accuracy of results within the study group.
Example: A drug trial includes only patients from one hospital that treats milder cases. The findings might not apply to all patients.
Sampling bias is a specific type of selection bias. It happens when the method of choosing participants makes some individuals more likely to be included than others, leading to a non-representative sample. This affects external validity—the applicability of results to the broader population.
Example: An online survey attracts tech-savvy participants, missing the views of those less familiar with technology.
In short: All sampling bias is a form of selection bias, but not all selection bias is sampling bias.
Selection bias is the broader term, covering any way a sample might not represent the population. Sampling bias specifically refers to issues in how the sample was chosen.
How to Avoid Sampling Bias in Research?
1. Define Your Population Clearly
Clearly define your target population to ensure every relevant group has a chance of being selected.
Example: If you’re studying customer satisfaction, define whether you’re targeting all customers or just recent purchasers, as including only recent ones can skew results.
2. Ensure Your Sampling Frame Matches the Population
The sampling frame (the list from which the sample is drawn) should accurately represent the entire population.
Example: If studying employee satisfaction, don’t just survey full-time employees; include part-timers and contractors.
3. Use Random Sampling Techniques
Random sampling ensures each member of the population has an equal chance of selection.
Example: In a school survey, randomly selecting students from all grades ensures fairness, rather than only selecting from one grade.
4. Avoid Convenience Sampling
Convenience sampling happens when you select participants simply because they are easy to access, which introduces bias.
Example: If a university only surveys psychology students, the results might be skewed since psychology students may have different views compared to other majors.
5. Use Stratified Sampling
Stratified sampling divides the population into subgroups and ensures each group is represented proportionately.
Example: If your population is 60% women and 40% men, a stratified sample would ensure your survey reflects this ratio.
6. Oversample Underrepresented Groups
To avoid under-coverage bias, oversample certain groups to get enough data.
Example: If Asian Americans only make up 5% of your population, you might oversample this group to ensure their views are accurately represented in the results.
How to Correct Sampling Bias, if it has Happened?
1. Reweight the Sample Data
Adjust the weights of responses from underrepresented groups to better reflect their proportion in the overall population.
Example: If you surveyed 1,000 people and only 100 were aged 60 or older (10%), but they should make up 25% of the population, adjust their responses so each represents 2.5 people.
2. Increase Sample Size
Increasing the number of participants from underrepresented groups can help balance the sample.
Example: If an environmental study surveyed 50 people from rural areas but 200 from urban areas, recruiting an additional 150 rural participants would balance the perspectives.
3. Sensitivity Analysis
This involves checking how results change when you tweak or add data to the analysis.
Example: Imagine you surveyed more males than females about favorite sports. By adding data for females or weighting their responses, Sensitivity Analysis could show how results differ with better representation.
4. Follow-Up on Non-Respondents
Following up with non-respondents can help correct imbalances in the sample.
Example: If only 30% of the initial survey respondents were women, but the target population is 50% women, sending reminders or offering incentives to non-responding women can increase participation.
5. Review the Sampling Frame
Ensure that the sampling frame includes all relevant segments of the population.
Example: If your study is about national healthcare access, but your sampling frame only includes urban hospitals, revise it to include rural clinics to capture rural populations as well.
Summary
Sometimes completely reducing sampling bias is impossible because every sampling method has a risk of introducing bias into the study.
The best approach is to always pretest or pilot test your study to identify potential biases or errors while conducting the survey.
After collecting the data, check for any bias in your data or sample.
For example, ensure the data doesn’t only reflect extreme cases and that the sample’s characteristics are similar to those of the overall population.
Checking for biases both before and after the survey will help you minimize errors and improve the reliability of your results.
Key Tips to Reduce Sampling Bias:
