Recognizing bias in research is not always easy. In an age of information overload, it can be difficult to discern which sources of data are trustworthy and which may have a hidden agenda. Research is often used as a way to promote or disprove certain ideas, theories, or points of view. In many cases, researchers will choose to conduct their own experiments rather than rely on existing research to support their point of view. This may seem like the ideal solution; after all, why trust researchers from another field who may have their own perspective when you can just study the same subject yourself? However, there are some pitfalls in this method. When conducting your own experiments, you must consider how introducing a new variable into your experiment might skew the outcome and make it biased toward proving your viewpoint rather than uncovering the true answer about what’s being researched. What is Bias in Research? Bias is a term used to describe a deviation from the expected or accepted norm in research. Bias can occur in any research methodology and can lead to misleading or misinterpreted results. Bias can be introduced during the research process in a variety of ways, including sampling bias, data analysis bias, and selection bias. - Sampling bias refers to the method of selecting a sample that is not representative of the population as a whole. For example, researchers may select participants from a specific geographical location or from a specific demographic group. In many cases, researchers will select participants who are most likely to produce desired results. This can introduce bias into the data by excluding other perspectives or information that would otherwise be available. - Data analysis bias occurs when researchers manipulate or select data to fit their desired outcome. This can include selectively choosing which data to include in the experiment or choosing which data to exclude from the experiment. Both types of data bias can lead to misleading or misinterpreted results. - Selection bias refers to the method of choosing the participants for a study. The researchers may choose participants who are most likely to produce desired results or participants who fit a specific profile. This can introduce bias into the data by only including perspectives that support a certain argument or excluding perspectives that may have produced different or alternative conclusions. Why is Bias Important? Even if you’re able to recognize bias in research, that doesn’t mean you can always account for it. Any research study will include some amount of bias, and the bias in one study is not necessarily the same bias in another study. Bias can be the difference between a conclusion that is based on a significant amount of data and one drawn from a single study; this can lead to a more generalizable and valid conclusion. If a researcher is unable to recognize and account for bias, their research may be misleading and/or invalid. Bias can be a significant problem in areas of research that are highly politicized or involve a degree of subjectivity. Researchers may be less conscious of bias in these fields because they tend to see their research as valid regardless of outside criticism. A biased sample or selection of data can also drastically skew the outcome of a study and make it less generalizable. Recognizing Biased Research - “Trust but verify”. A good rule of thumb when evaluating research is to “trust but verify” by looking for signs of bias and other potential issues. If a study fails to meet your personal standard of quality, then you should trust what the data shows but verify that the data has been interpreted correctly. - Be wary of shocking headlines. All too often, sensational headlines are used to draw people into a story. The study itself may not contain any bias, but the journalists who write about it will often include a shocking conclusion as a way to draw readers in. Be wary of these types of stories and don’t jump to conclusions based on one study, particularly if the headline is dramatic. - Look to see who funded the research. Reputable research will often have the source of funding listed in the acknowledgement or at the end of the paper. While this doesn’t guarantee that the research is unbiased, it does make it easier to determine who may have had a hand in producing the data. Bias in Meta-Research and Surveys Meta-research studies other research. This is good in that it can offer a broader perspective on the current state of research and help us see where we have more data or where we need more data. However, it can also lead to bias when the researchers are not careful. For example, researchers may decide to review only certain types of data or only certain types of studies. This can skew their results and make them less valid. What’s more, meta-research often has a political agenda as well. - Political agenda. Meta-research is usually done with an agenda in mind. It’s generally being done to suggest how research should be done in the future. As such, researchers are generally looking for what will support their agenda and what will be most helpful to their cause. - Selective review. Meta-research studies other research, but the researchers may choose to review only certain types of studies or only certain types of data. This can skew their results and make them less valid. What’s more, meta-research often has a political agenda as well. Bias in Experimentation With experimentation, researchers are actively introducing a new variable into their experiment to see how it affects the outcome. In doing so, they must first decide what the variable is and then make sure that it is implemented consistently. In many cases, researchers will use the same materials and equipment in each experiment to ensure that their results are consistent. This is a good idea, but it can also lead to certain biases entering into the experiment. - Experimenter effect. Researchers often want to make sure that the variables in their experiment are consistent from experiment to experiment. However, the very act of doing so may introduce bias into the experiment. - The experimenter effect refers to how the experimenter’s expectations or knowledge of the outcome can affect the results of the experiment. This can be particularly problematic when the experimenter is trying to be consistent but doesn’t discover that he or she has some expectation or knowledge of what the results should be. - Replication failure. Experiments that are not replicated properly can introduce a significant amount of bias into the data. What’s more, they can prevent the data from being generalizable. This can be particularly problematic in fields where there is little consistency or regulation. For example, psychology and neuroscience are fields where replication failures are particularly common. Bias in Interpretation and Reporting Experiments will often have to be conducted by several different researchers in different labs. This can be a good thing in that it can help to ensure that the findings are valid. However, researchers may still introduce bias into their interpretation and reporting of the results. - Interpretation bias. Researchers may be biased in how they interpret the data that they collect. This can lead to misleading conclusions and can also make the data less generalizable. - Reporting bias. Researchers may also be biased in how they report their conclusions. This can include selecting certain data to report and excluding data that challenges the overall conclusion that they want to draw from the experiment. Key Takeaway Bias is a significant problem in areas of research that are highly politicized or involve a degree of subjectivity. Researchers may be less conscious of bias in these fields because they tend to see their research as valid regardless of outside criticism. You can avoid falling into the trap of biased research by being wary of shocking headlines and looking for signs of bias in the studies that you read. Remember, “trust but verify” and you’ll be better able to recognize bias in research and make more informed decisions about what information you trust.