Is the way you think biased? Here are 3 common mistakes to watch out for

In a world of information overload, we can fall victim to all sorts of cognitive biases. Since they can lead us to generate false conclusions, it’s particularly important to understand what these biases are and how they work, as the consequences can become quite drastic.

Confirmation bias, sampling bias, and brilliance bias are three examples that can affect our ability to critically engage with information. Jono Hey of Sketchplanations walks us through these cognitive bias examples, to help us better understand how they influence our day-to-day lives.

One of the most-commonly encountered and understood, you’re likely to have already heard about confirmation bias. This cognitive bias affects the way we test and evaluate hypotheses every day.

In simple terms, confirmation bias is the tendency to seek out or interpret evidence in such a way that supports our own strongly-held beliefs or expectations. This means that, given access to the same set of data and information, different people can come to wildly differing conclusions.

Feeding into confirmation bias can lead us to make ill-informed choices or even reinforce negative stereotypes. For this reason, it is important to remember to seek out information that both confirms and contradicts your presumptions about a certain topic.

Sampling bias is a kind of bias that allows us to come to faulty conclusions based on inaccurate sample groups or data. Generally, the cause of sample bias is in poor study design and data collection.

When polling individuals for survey questions, it is important to get a representative picture of an entire population. But this can prove surprisingly difficult when the people generating the study are also prone to human flaws, including cognitive biases.

A common example involves conducting a survey on which political party is likely to win an election. If the study is run by a professor who only polls college students, since they are around and therefore easier to collect information from, the poll will not accurately reflect the opinions of the general population.

To avoid sampling bias, it is important to randomize data collection to ensure responses are not skewed towards individuals with similar characteristics.

Brilliance bias is another common cognitive bias that makes us more likely to think of genius as a masculine trait. This is in part due to the lack of female representation in both traditional academic and executive positions.

In fact, The Journal of Experimental Social Psychology published an in-depth study on brilliance bias in 2020. It suggests that a likely source of this bias is in the uneven distribution of men and women across careers typically associated with higher level intelligence.

While this distribution is a remnant of historical factors that limited access to education and career choices for women in the past, its presence has made us (wrongly) conclude that women are less brilliant instead. Naturally, as the cycle perpetuates the uneven distribution of women in these careers, it only reinforces this bias.

These few examples from Jono Hey give a good overview of some of the biases we face when trying to understand the data given to us, but they are just the tip of the iceberg.

It is important to be cognizant of these biases in an era where we are constantly engaging with information, especially if we want to combat some of the harmful consequences they entail.