What is Bias in Sampling – Types & Examples

Sampling is necessary as it is impossible to make large population surveys to deduct results. It will be hard to measure the exact parameter of interest. Therefore, researchers do sampling. They try their best to draw a sample representing most of the population. Unfortunately, there occurs an issue which is termed bias in sampling.

What is bias in sampling? How it occurs? What are the types of bias in sampling? What are some examples of bias in sampling? How can one avoid bias in sampling? All these queries will be discussed in this blog. But, before that, let’s understand the definition of bias in sampling in detail. 

What is Bias in Sampling?

One can define bias in sampling as ‘’when specific people or aspects are more likely to be included or omitted from the sample; bias in sampling refers to the systematic deviation from a representative sample that results in an unrepresentative or skewed picture of the population under study.’’

What are the Causes of Bias in Sampling?

• It is introduced if the sampling technique is not genuinely random. 
• It happens if the standards used to choose the sample’s participants do not represent the general population.
• It happens when a sizable portion of the chosen people choose not to participate or respond. 
• It can happen when people choose to participate in a study independently. 

How Many Types of Bias in Sampling are There?

There are many different types of bias in sampling. Some of these are discussed below.

• Selection Bias

This happens when particular people or parts of the population have a greater or lesser chance of being represented in the sample. Non-random sampling techniques, including convenience or voluntary response sampling, may be to blame. The results may be skewed by the over- or underrepresentation of particular groups due to selection bias.

Example of Selection Bias

Let’s say a researcher wishes to investigate the city’s typical household income. The researcher selectively selects homes in rich neighbourhoods instead of randomly picking them. As a result, higher-income households would be overrepresented, and the genuine average income of the overall population would be understated.

• Non-Response Bias

Non-response bias happens when people chosen for the sample don’t engage in the study or respond. If individuals who choose not to react differ in some ways from those who do, this can induce bias. Estimates may be skewed because the final sample may not reflect the full population accurately.

Example of Non-response Bias

Consider conducting a poll to gauge people’s attitudes on a political topic. However, only people with strong beliefs tend to reply to the poll, whereas people with moderate viewpoints opt-out. A distorted assessment of popular sentiment may result from the survey results failing to reflect the wide range of opinions in the population adequately.

• Measurement bias

When a measurement tool or method used to gather data consistently deviates from the true value, it is said to have measurement bias. It may be brought on by inaccuracies in the data collection process, problems with the instrument’s calibration, or the researcher’s bias. Measurement bias can misrepresent the connection between variables and produce false findings.

Example of Measurement Bias

Consider a scenario in which a researcher compares the effectiveness of Programme A and Programme B in two separate studies. At the start and completion of the programme, participants are expected to self-report their weight. However, compared to individuals in Programme B, participants in Programme A regularly overestimate their weight after the programme due to measurement bias.

Because the participants in each programme have distinct reporting tendencies, measurement bias is introduced in this case. Participants in Programme A may be more inclined to exaggerate their weight or may need help to measure it precisely, leading to persistently lower reported weights. Participants in Programme B might be more truthful or precise in their weight reporting.

• Survivorship Bias 

Survivorship bias occurs when a sample only contains a subset of people or instances due to the exclusion of others. Results may be distorted as a result, especially when examining populations or phenomena where leaving out some examples greatly impacts the overall picture.

Example of Survivorship Bias 

Survivorship bias is apparent when an analysis only looks at the performance of businesses that have successfully gone public (IPOs) and ignores businesses that have failed or never gone public. This bias can skew results because it ignores the bigger pool of unsuccessful businesses. The following analysis suggests that startups in the tech sector have a greater success rate. It is critical to consider successful and unsuccessful examples to provide a more realistic picture of the broader environment and address survivorship bias.

• Sampling Frame Bias 

Sampling frame bias develops when the sampling frame used to choose the sample does not represent the target population fairly. It may result from skewed estimates caused by excluding particular demographic groups or obsolete or incomplete sample frames.

Example of Sampling Frame Bias 

Let’s think of a study on smartphone use among university students. The researcher receives the sampling frame from the student directory of a specific university. The study would suffer from sample frame bias if the directory left out students from other colleges or unenrolled people who matched the target demographic.

Due to the sample size and incomplete representation of the intended demographic, the results need to be more generalisable to the total population of college students.

How to Avoid Bias in Sampling?

Here are some of the ways to avoid bias in sampling. 

Random Sampling

Make that every member of the target population has an equal chance of being chosen for the sample by using random sampling techniques. By doing this, selection bias is reduced, and a representative sample is obtained.

Stratified Sampling

Based on certain factors (such as age, gender, and geography), divide the population into pertinent subgroups and randomly sample from each stratum. This guarantees that various population groupings are represented.

Oversampling and Undersampling

Researchers can oversample certain subgroups if they are underrepresented or of particular interest to achieve a large enough sample size. Unlike oversampling, undersampling can be used to lower some groups’ representation purposefully.

Systematic Sampling

Choosing the nth person from the population can aid in obtaining a representative sample. The sampling interval is chosen based on the intended sample size and population.

Cluster Sampling

Make groups or clusters from the population (such as neighbourhoods or schools) and randomly choose a few. After that, sample each person inside the chosen clusters. When the population is spread out geographically, this method is helpful.

Avoid Convenience Sampling

Bias can be introduced via convenience sampling, which involves selecting people who are easily accessible. Instead, researchers should strive for systematic or random sampling.

Use of Sampling Frames

Ensure the sampling frame includes all relevant data and fairly depicts the target population. The bias introduced by the sample frame can be reduced through routine updates and verification.

Transparent Reporting

Document the sampling procedure thoroughly, giving information on the sample size, the sampling process, and any exclusions or restrictions. This encourages transparency and enables readers to evaluate the study’s potential for bias.

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