Let’s talk about cognitive biases: EXPECTANCY BIAS
The expectancy bias describes the tendency for researchers to believe and publish data that agree with their expectations for the outcome of an experiment, and to discard or downgrade the corresponding weightings for data that appear to conflict with those expectations.
The expectancy bias presents itself due to demand characteristics—which are given by the researcher to the participant about the nature of the study. What does this mean? The researcher interjects their subtle cues of their personal confirmation bias to the participants. Which then is played out when the researcher collects and interprets data in a way that confirms their hypothesis and ignores contradictory information.
This cognitive bias is often overlooked by people, but is one reason why (1) knowing who is conducting research is important and (2) whether there was researcher blinding in a study. If someone knows which group receives which treatment, then they may subconsciously interpret data and alter their interactions with those individuals to generate more favorable data for their hypothesis.
As an example, in a paper Alex is coauthoring on how ketogenic diets impact sports performance, the only studies noting benefits for endurance exercise performance are those conducted by known ketogenic diet advocates. When tested by researchers without established biases, the effects are either neutral or detrimental.
Now how do we avoid this bias?
The easiest way to overcome this bias is to remove yourself from the research if you are a researcher (e.g., double-blind studies). This way the experimenters beliefs don’t influence the participants behaviors. If you’re a layperson not conducting research, then be aware that everyone has a subconscious bias towards their belief systems and that they can be overcome by proper methodological design.
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