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Biases and lies in research

Researchers are just human, and so elements of bias can creep into research. Here we examine some common types.
A group of students working together
© Joanne O’Brien, University of Westminster

Researchers want to ensure that the work they do is fair and unbiased. Meaning that when research is carried out care is taken to ensure that the researchers own feelings do spoil the results of the research.

However, researchers are just human, and so elements of bias can creep into research. It is important to know about these biases so that we can look for trustworthy things to read and also avoid being biased in our own research.

Some of the common types of bias in research are:

Confirmation bias

Here the researcher gives more value to data that supports their already held view of the world. The researcher is more likely to reject information that does not match their viewpoint. For example, if a researcher thinks that social media is harmful for young people, they might think people who say they don’t have any negative effects from social media use are not being truthful.

Cultural bias

Here a researcher judges other cultures based on their own values and standards. A researcher from a very wealthy background may find it difficult to understand how people in poverty live and why they make certain decisions, leading to those decisions being labelled as wrong or irresponsible.

Leading word bias

You might have heard of this one already in your studies, this is where the researcher asks questions in a way that makes people answer a particular way. This could be on a survey or in interviews, but will cause problems in the data. For example asking you ‘do you think bananas are the best fruit?’ is more likely to get results that are positive about bananas than if we just asked ‘which do you think is the best fruit?’

Funding bias

Research is very expensive, so will often need outside funding to support it. However, this funding can compromise the work that is being done, or can work to actively produce results that support a particular industry or company. A famous example of this is research funded by tobacco companies that suggests smoking is not bad for you. This research was funded to try and make people think smoking was okay, so the tobacco companies could make more money – but we now know that these studies were not accurate.

These kinds of biases can cause issues for us as researchers, both when we are doing our own research, but also when we are looking at other people’s research.

When we are reading other people’s research we need to keep an open mind, but we also need to think about who makes for a trustworthy source.

© University of Westminster
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