{"id":125218,"date":"2021-10-27T22:24:10","date_gmt":"2021-10-28T05:24:10","guid":{"rendered":"https:\/\/www.questionpro.com\/blog\/?p=125218"},"modified":"2025-07-01T22:27:54","modified_gmt":"2025-07-02T05:27:54","slug":"sampling-bias","status":"publish","type":"post","link":"https:\/\/www.questionpro.com\/blog\/sampling-bias\/","title":{"rendered":"Sampling Bias: Understanding It & How to Avoid It + Examples"},"content":{"rendered":"\n

Imagine conducting a research study on the experience of driving a car, but all the participants are exclusively motorcycle users; inevitably, the results will be useless for the research we aimed for. This type of\u00a0sampling error\u00a0that occurs when conducting a research study with a poor selection of participants is known as\u00a0<\/strong>sampling bias, and it\u00a0<\/span>can be prevented by always selecting participants at random and from different contexts.<\/p>\n\n\n\n\n\n

What is Sampling Bias?<\/h2>\n\n\n\n

Sampling bias, or a biased sample in research, occurs when members of the intended population are selected incorrectly\u2014either because they have a lower or a higher chance of being selected. It<\/span> is a type of sampling error<\/a>.<\/p>\n\n\n\n

The most popular and easily understandable example of sampling bias is Presidential election voters. If you poll 1000 middle-class, blue-collar voters, the sample will be heavily biased because it won\u2019t be diverse enough to paint the whole picture. It leaves out multiple demographics that are necessary to draw an accurate conclusion.<\/span><\/p>\n\n\n\n

Causes and Types of Sampling Bias<\/h2>\n\n\n\n

There are many causes of bias in sampling that researchers need to keep an eye out for. Here are the most common ones: <\/span><\/p>\n\n\n\n

Undercoverage and Sampling Bias:\u00a0 <\/h3>\n\n\n\n

Undercoverage is one of the biggest causes of sampling bias because researchers fail to represent the sample accurately. The main reasons for this undercoverage are<\/span> inadequately representing the population or collecting responses only from readily available respondents by using convenience sampling<\/a>.<\/span><\/p>\n\n\n\n

Running national surveys online fall under this risk category of undercoverage because they tend to miss out on the elderly and those with limited or no access to the internet.<\/span><\/p>\n\n\n\n

A fair representation of the population helps you get accurate results of the survey<\/a>. But it means that you need to make extra efforts to ensure you do not miss out on different demographics.\u00a0<\/span><\/p>\n\n\n\n

Example of undercoverage bias: <\/em><\/h4>\n\n\n\n

Researchers want to understand the effect of a new traffic law in a city,<\/span> so they conduct a\u00a0survey sampling<\/a>\u00a0via a convenience mall. The study is highly likely to suffer undercoverage from the following groups:<\/span><\/p>\n\n\n\n