Back

# Sampling Error Vs Sampling Bias: All You Need To Know

Sampling is a commonly used technique in statistical research. It involves selecting a subset of individuals from a larger population to represent it accurately.

However, things get tricky, as they always do when you analyze the results.

Errors such as sampling biases and sampling errors can pop up can turn your research or survey worthless if you’re not careful. Often these errors are thought to be the same and are used interchangeably.

Though these errors can produce unreliable results, the truth is they are poles apart. It raises forth questions such as, what is ‘sampling errors vs. sampling bias?’

And how can you prevent these errors?

Well, don’t worry. We’re here to help!

In this blog, we’ll go through the definitions of both terms, look at a couple of examples, and examine how these terms overlap and differ from each other.

Table of Contents

## Sampling Error Definition

Researchers use sampling errors to measure differences between a population’s characteristics and a sample’s.

You cannot avoid this error as you have a limited sample size. According to OECD, you’ll never be able to accurately represent the actual population as it is more diverse and larger than the sample size.

### Example of Sampling Error

Assume that a researcher wants to estimate the average height of all individuals in a population using a sample of 100 people.

However, if this sample includes a higher number of taller individuals than the remaining population, it will cause an overestimation of  the average height.

### Sampling Bias Definition

Sampling bias in research results from choosing a sample that is not random or fails to represent the rest of the population. Similarly to sampling error, sampling bias also leads to inaccurate conclusions.

Sampling bias occurs when the research data is gathered unfairly. In such cases, certain individuals in the target population have a higher or lower chance of sampling probability than others.

This error often happens unintentionally and can be missed by the researcher. However, you can access the survey once it’s done.

``ALSO READ: Representative Samples: Importance + Methods ``

### Example of Sampling Bias

Suppose a researcher wants to study the level of physical activity among adults in a city. The researcher chooses to survey participants at a local gym during peak hours.

This sample, however, is likely to be biased.

Here’s why.

Because it targets individuals who are already active and have the time and resources to go to the gym. Moreover, this sample excludes other segments of the population like older adults, individuals with disabilities, and low-income individuals.

``ALSO READ: The Right Time and Way To Do Quota Sampling ``

## Types Of Sampling Bias

The table below mentions some of the key sampling bias types:

## Sampling Bias vs Sampling Error: Differences

The table below explains the differences between sampling bias and sampling error:

## Sampling Bias vs Sampling Error: Similarities

Now that you’re familiar with the differences between sampling bias and sampling errors, you must also understand their similarities.

1. Both errors can lead to inaccurate conclusions and should be minimized to obtain reliable results.
1. You can also reduce both errors by increasing the sample size and using proper sampling techniques.
``ALSO READ: Biases in Surveys: What You Need to Know ``

## How To Avoid Sampling Bias?

Completely avoiding sampling bias might just be a little too much. But you can control it to a certain degree. You can prevent bias sampling by following these tips:

• Clearly outline your target population and the sample frame
• Make sure that your sample frame accurately represents the target population
• Avoid long and complicated surveys
• Make your surveys accessible to the average individual
• Follow up on your initial survey
• Avoid convenience sampling as much as you can
• Define your survey goals
• Ensure your participants get an equal representation

### Conclusion

Sampling error and sampling bias are two common pitfalls that are bound to occur in your research studies. While sampling error is due to chance, sampling bias is due to systematic errors in the sampling process.

But the good thing is you can minimize these errors through proper sampling techniques and careful consideration of the sampling frame.

By reducing these errors, you can obtain reliable results that accurately represent the population of interest.

#### Interested In Sending Your Own Surveys?

Explore our solutions that help researchers collect accurate insights, boost ROI, and retain respondents using pre-built templates that don’t require coding.

###### Survey Point Team
Experience SurveyPoint for Free
No Credit card required
Try our 14 day free trial and get access to our latest features
blog popup form
Experience SurveyPoint for Free
No Credit card required
Try our 14 day free trial and get access to our latest features
blog popup form