What do researchers worry about? The sampling error.
This blog will educate you what sampling error means and how to reduce it,
Because if you don’t reduce this error,
Your research outcome will go to the trash, and no researcher on this planet wants that.
So let’s eliminate this error, before it even steps on your face. Go.
What is sampling?
Key Points:
Sampling is a technique used in statistics to gather data from a small group (sample) selected from a larger group (population).
Instead of surveying every individual in the population, researchers select a portion of it to make estimates and draw conclusions about the whole.
Sampling is essential when it’s impractical or impossible to examine the entire population due to factors like time, cost, or accessibility.
The goal of sampling is to choose a group that accurately represents the larger population.
The process of selecting the sample can vary based on the sampling method used, such as random sampling, stratified sampling, or cluster sampling, each having specific use cases depending on the research objective.
However, even with the best methods, there’s always the risk that the sample may not perfectly reflect the population, leading to potential errors, like sampling error.
What is sampling error?
Key Points:
Sampling error occurs when the sample selected doesn’t perfectly represent the population, leading to differences between the sample’s results and the population’s true opinion, answers and data.
Even when a sample is selected randomly and carefully, it may not perfectly reflect the population, causing this error.
For example, if you’re conducting a survey about people’s favorite ice cream flavors and only sample a small group from a large city, the results may not perfectly match what you would find if you asked everyone in the city.
This difference between your sample’s results and the actual preferences of the whole population is the sampling error.
Types of Sampling Errors
S.No | Type of Sampling Error | What It Is | Example |
---|---|---|---|
1 | Population-Specific Error | Occurs when the researcher does not correctly identify the population to study, resulting in data from a group that does not represent the intended population. | A company surveys middle-income consumers instead of high-end luxury goods buyers, leading to irrelevant findings about the target market. |
2 | Selection Error | Happens when the sample is not randomly chosen or when participants self-select, causing a biased sample that doesn’t represent the population. | Surveying only people who follow the health ministry’s social media account about health policies, which excludes less informed or uninterested individuals. |
3 | Sampling Frame Error | Occurs when the list (sampling frame) from which a sample is drawn is incomplete or inaccurate, leading to a non-representative sample. | Using landline phone numbers to survey internet habits excludes people who don’t have landlines, such as younger, tech-savvy individuals. |
4 | Non-Response Error | Happens when a significant portion of the sampled individuals do not respond, leading to a biased sample that excludes their perspectives. | In a customer satisfaction survey, only very satisfied or dissatisfied customers respond, missing the opinions of neutral customers. |
5 | Random Sampling Error | Arises due to chance when a randomly selected sample does not perfectly represent the population; the larger the sample, the smaller this error. | Randomly selecting 100 people for a survey about a new park but overrepresenting young adults, while the population has more older residents. |
6 | Systematic Sampling Error | Occurs when the sampling method has a consistent bias, such as excluding certain groups or using a flawed selection process, resulting in skewed results. | Conducting a survey on eating habits during the day excludes working individuals, overrepresenting retirees and stay-at-home parents. |
Difference Between the sampling and non-sampling error
Aspect | Sampling Error | Non-Sampling Error |
---|---|---|
Definition | The error that occurs when the sample does not accurately represent the population due to chance. | Errors that occur due to issues not related to the sampling process, such as data collection mistakes, measurement errors, or biased survey design. |
Cause | Arises because a sample is only a part of the population, not the whole population. | Caused by flaws in the survey method, interviewer bias, data processing errors, or poor question design. |
Types |
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Reducibility | Can be reduced by increasing sample size or using better sampling techniques. | Can be reduced by improving survey design, data collection methods, and ensuring unbiased procedures. |
Source | Inherent to the act of sampling (because the sample is not the full population). | External issues unrelated to the sampling process, such as faulty instruments or human error. |
Example | Surveying a small group that, by chance, doesn’t represent the full diversity of the population. | Failing to reach certain groups of people for responses or using poorly worded questions that lead to misinterpretation. |
Difference Between the sampling error and sampling bias
Aspect | Sampling Error | Sampling Bias |
---|---|---|
Definition | The natural error that arises when a sample doesn’t perfectly represent the population due to chance. | A systematic error that occurs when the sampling process consistently favors certain groups over others. |
Cause | Occurs by chance because a sample is only a subset of the population, not the whole population. | Caused by flawed sampling methods, where certain groups are overrepresented or underrepresented. |
Nature | Random and unavoidable to some extent, though it can be minimized. | Systematic and avoidable by improving the sampling method. |
Reducibility | Reduced by increasing the sample size or using better sampling techniques. | Eliminated by using proper random sampling techniques and ensuring no inherent bias in sample selection. |
Impact on Results | May lead to minor deviations from the population’s actual characteristics but does not consistently skew results in one direction. | Skews results in a particular direction, leading to inaccurate and biased conclusions. |
Example | Selecting 100 random people from a population, but by chance, it includes more young people than the actual population proportion. | Conducting a survey only among college students to represent the entire city’s opinion, over-representing younger respondents. |
What causes sampling error?
1. Inadequate Sample Size:
A small sample size is one of the main reasons for sampling error.
When you use too few people in your sample, it’s more likely that they won’t represent the whole population.
A small group might not capture all the differences in the population, which can lead to wrong conclusions.
Why it Causes Error: Small samples are less likely to have a mix of different characteristics, opinions, or behaviors that are found in the full population.
Because of this, the results from a small group can be quite different from what you’d see if you studied everyone.
Example: Imagine you want to find out how people in a city get around, but you only ask 30 people out of a city of 100,000.
The answers from just 30 people might not show the real preferences of everyone in the city, which could lead to wrong ideas about how people travel.
2. Non-Random Sampling:
Non-random sampling happens when some people or groups are picked more often than others.
This leads to bias because the sample no longer represents the whole population.
A good random sample makes sure that everyone has an equal chance of being chosen, which helps lower sampling errors.
Why it Causes Error: If the sample isn’t random, it can create systemic bias. Some groups might be included too much, while others are left out.
This makes the results unbalanced.
Example: Let’s say a researcher only surveys people at a shopping mall. They might end up with too many younger or middle-income people in their sample.
They would miss people who don’t usually go to malls, like seniors or those with lower incomes.
This makes the results about shopping habits biased and not accurate for the whole population.
3. Population Variability:
Populations with a lot of differences (like age, income, or education) are harder to sample accurately.
If the sample doesn’t capture all these differences, it can cause big sampling errors.
Why it Causes Error: When the population is very diverse, a random sample might miss important groups.
This leads to results that aren’t complete or accurate.
The more variety there is in the population, the bigger the sample needs to be to include all these differences.
Example: Imagine you are studying people’s eating habits, but the group includes different ethnicities with unique diets.
If you use a small or poorly chosen sample, you might miss some of these cultural food choices.
This would lead to conclusions that don’t show the real eating habits of everyone.
4. Time-Related Bias:
Doing a survey at a specific time or during a special event can lead to sampling errors.
During certain times, people might act differently, and their answers may not show their usual behavior.
Why it Causes Error: Time-related bias affects how random the sample really is.
For example, if you do a survey during a national holiday, people might answer based on holiday activities instead of their normal habits.
Example: Imagine you survey people about their TV habits during the Super Bowl.
You’d probably get a lot of people saying they’re watching sports.
This could lead to a biased result that doesn’t match their normal TV habits.
5. Selection Error:
This happens when there is a problem in how people are picked for the survey, like when people choose to join themselves.
When people volunteer, they may be very different from the whole group in terms of interest or behavior.
Why it Causes Error: Selection error causes bias because those who decide to join often have strong opinions or are different from those who don’t join.
This means the sample doesn’t truly represent the whole population.
Example: Imagine a company sends an online survey to ask customers for feedback.
Only the very happy or very unhappy customers might reply.
This makes the sample biased and could give a wrong idea about overall customer satisfaction.
How to recognize the sampling error once it happens?
1. Discrepancy Between Sample Results and Population Data
What to Look For: A clear sign of sampling error is when your sample results are very different from what you know about the whole population. If there is existing data about the population, you can compare your sample’s data to it.
Why It Matters: If there is a big difference, it shows that your sample may not represent the population well. For example, if census data says 40% of people are under 30, but your sample only has 20% under 30, it’s likely due to sampling error.
Action: Compare key things like age, gender, and income in your sample to the known data about the population to see if there are any big differences.
2. High Margin of Error or Wide Confidence Intervals
What to Look For: Another sign of sampling error is a high margin of error or wideconfidence intervals in your results. The margin of error shows how much uncertainty there is in your sample’s findings.
Why It Matters: A wide confidence interval means the sample might not represent the population well. If the margin of error is big, it means the results might not be very accurate for the whole group.
Action: Calculate the margin of error and confidence intervals for your study. If they are too wide, think about using a bigger sample size to make your results more accurate.
3. Unusual or Extreme Results
What to Look For: If your sample results seem too extreme or don’t match what you’d expect, it might mean there’s sampling error.
For example, if you survey political preferences in a mostly moderate area but find most people support a radical candidate, something might be off.
Why It Matters: Extreme results can mean the sample wasn’t a good match for the population.
These unusual results could happen if the sample was too small or not chosen randomly.
Action: Check your sample result for anything strange or unusual that might show sampling error.
If you can, take a new sample or increase your sample size to fix these problems.
4. Large Variability Between Different Samples
What to Look For: If you do several surveys or studies with different groups from the same population and get very different results each time, it could mean there’s sampling error.
Why It Matters: When different samples give very different results, it shows that the samples might not be capturing the population accurately. This is a sign that sampling error is affecting your findings.
Action: To reduce sampling errors, try using methods like stratified sampling orbootstrapping to make your samples more representative.
Also, run ananalysis of variance (ANOVA) to check if results are consistent across different groups.
If you find big differences, review your sampling method to make sure it matches the population well.
5. Non-Representative Demographics or Skewed Subgroups
What to Look For: Check the demographic details of your sample. Does it match what you know about the whole population?
If some groups are overrepresented or underrepresented—like too many people from one age group or area—it’s a clear sign of sampling error.
Why It Matters: Unbalanced demographics mean that your sample doesn’t represent the population well, which can lead to biased results.
Action: Compare your sample’s demographics to the population data. If there are big differences, you might need to adjust how you sample or reweight the data to make it more accurate.
6. Small Sample Size
What to Look For: Even if the results seem okay, if the sample size is too small, there’s a good chance you have sampling error.
Why It Matters: Small samples mean random chance can easily lead to a poor representation of the whole population. The smaller the sample, the less likely it is to show the true diversity of the group.
Action: Check if your sample size is big enough for the population you’re studying. If it’s too small, increase the sample size to lower the risk of sampling error.
7. Non-Random Sampling Issues
What to Look For: Did you pick your sample in a way that was biased? If some groups were chosen more often—on purpose or by accident—this could cause sampling error.
Why It Matters: If the sampling wasn’t random or if some groups had a better chance of being picked, the results won’t fairly represent the population. Non-random sampling can mess up the data and lead to wrong conclusions.
Action: Review how you picked your sample. If it wasn’t random, change your method for future studies. Also, try to fix any bias with statistical adjustments if you can.
8. Disproportionate Subgroup Representation
What to Look For: If certain groups in your sample are represented too much or too little, it can create an imbalance.
Why It Matters: If your sample has too many people from one group (like one area or income level), your results will mostly reflect that group’s views.
This adds error to your findings.
Action: Compare the groups in your sample to the actual population. If some groups are over- or underrepresented, adjust the weights in your sample or use stratified sampling in future studies.
9. Use of Biased Sampling Frames
What to Look For: If the list you used to pick your sample (calledthe sampling frame) was missing people or had errors, it could cause sampling errors.
Why It Matters: If the list doesn’t include everyone or leaves out certain groups, your sample won’t reflect the whole population.
This leads to errors in your results.
Action: Make sure the sampling frame is complete and up to date. If it was missing groups or was biased, fix it by adding the missing groups or changing how you pick your sample.
How to reduce the sampling error?
1. Increase Sample Size: Larger samples better represent the population and reduce the chance of error.
2. Use Random Sampling: Ensuring that every member of the population has an equal chance of selection minimizes bias.
3. Stratified Sampling: Divide the population into subgroups and sample from each to ensure all groups are represented.
4. Check and Improve the Sampling Frame: Ensure the list from which you are sampling is complete and up-to-date.
5. Replication: Repeating the study multiple times with different samples can identify and reduce random variations.
6. Use Proper Timing: Avoid sampling during special times (e.g., holidays or big events) to prevent seasonal bias.
7. Weighting Adjustments: Apply weights to correct imbalances in overrepresented groups, ensuring the sample reflects the true population.
8. Pilot Testing: Conduct a pretest to find and fix problems in the sampling process before the full study.
1. Increase Sample Size
One of the best ways to reduce sampling error is by using a bigger sample.
A larger sample is more likely to show the true diversity of the population. It captures more differences and gives better results.
Why It Works: When you increase the sample size, the chance that it truly represents the population also goes up.
Random chance has less of an effect in larger samples.
Example: Imagine you want to find the average household income in a big city.
A sample of just 50 households might not show the full income range, but if you use 500 households, you’re much more likely to get an accurate picture.
2. Use Random Sampling
Random sampling means everyone in the population has an equal chance of being picked.
This helps avoid bias and makes sure no group is over-represented.
Why It Works: Random sampling stops selection bias.
It helps the sample represent the whole population better and reduces the chance of leaning too much toward one group.
Example: Instead of picking survey respondents from just one shopping mall (which is convenience sampling), you can randomly pick people from different parts of a city.
This way, the sample will better represent the whole population.
3. Use Stratified Sampling
Stratified sampling means dividing the population into different groups (called strata) based on things like age, gender, or income, and then taking a sample from each group.
Why It Works: This method makes sure all the different groups in the population are included in the sample. It helps avoid missing or over-representing any group.
Example: If you’re studying voting preferences, you can divide people into age groups and then sample from each group.
This way, both younger and older voters are fairly represented in your results.
4. Check and Improve the Sampling Frame
A sampling frame is the list you use to pick your sample.
Making sure this list is accurate, complete, and up-to-date is important so you don’t leave out parts of the population.
Why It Works: If the sampling frame is missing key groups, the sample won’t represent the whole population well, which leads to sampling errors. Keeping the list complete and up-to-date helps prevent these errors.
Example: If you’re doing a survey about internet use, you should include people without internet access too, like those who only use landlines.
This way, the sample won’t be biased toward just internet users.
5. Replication
Repeating a study with different samples can help reduce sampling error.
By comparing the results of each sample, you can see how much random error there is.
Why It Works: Doing the study multiple times helps balance out random errors. This makes the final results more reliable and easier to trust.
Example: If you run several surveys on what consumers like, in different areas and at different times, you can get rid of random differences.
This gives you a more accurate idea of what people really prefer.
6. Use Larger and Diverse Subgroups
Make sure your sample has a wide range of people, especially when the population is diverse in things like income, education, or lifestyle.
Using bigger and more varied groups helps you capture all the differences in the population.
Why It Works: The more diverse the sample, the better it will represent the whole population.
Dividing the sample based on important demographics will help reduce bias and error.
Example: If you’re doing a health survey for the whole country, include people from both urban and rural areas, all age groups, and different income levels.
This way, you get a complete picture of everyone’s health habits.
7. Use Proper Timing and Avoid Seasonal Bias
Don’t do your sampling during times that could cause bias, like holidays, big events, or extreme weather.
These times can affect some groups more than others.
Why It Works: Surveying at normal, neutral times helps avoid errors caused by special events that might temporarily change how people think or act.
Example: If you survey travel habits during the winter holidays, you might end up with too many holiday travelers and miss those who don’t travel.
Surveying at regular times gives a better view of everyone’s travel habits all year.
8. Weighting Adjustments
If some groups are overrepresented in a sample, researchers can use a method called weighting to fix these imbalances.
Why It Works: Weighting helps correct the sample so it better matches the actual population. It makes the results more accurate.
Example: Imagine a survey of city residents ends up with too many young people.
You can use weights to lower their impact and make sure older people’s views are counted fairly too.
9. Pilot Testing
Doing a pilot study or pretest helps you find problems in your sampling process before running the full study.
Why It Works: Pilot testing lets you spot and fix sampling errors early.
This way, you can adjust your methods before starting a bigger and more costly study.
Example: A pilot survey might show that some groups are hard to reach.
This can help the researcher change the sampling method to include those groups better in the main study.
Bottom Line
Sampling error occurs when the sample selected doesn’t perfectly represent the population.
That means,
When the sample does not match the population’s characteristics (like age, income, and job), which is essential for researchers to obtain reliable results, answers, or insights from their studies.
And this is how you reduce the sampling error,
Increase Sample Size: Larger samples better represent the population and reduce the chance of error.
Use Random Sampling: Ensuring that every member of the population has an equal chance of selection minimizes bias.
Stratified Sampling: Divide the population into subgroups and sample from each to ensure all groups are represented.
Check and Improve the Sampling Frame: Ensure the list from which you are sampling is complete and up-to-date.
Replication: Repeating the study multiple times with different samples can identify and reduce random variations.
Use Proper Timing: Avoid sampling during special times (e.g., holidays or big events) to prevent seasonal bias.
Weighting Adjustments: Apply weights to correct imbalances in overrepresented groups, ensuring the sample reflects the true population.
Pilot Testing: Conduct a pretest to find and fix problems in the sampling process before the full study.