Results: This is about surveys with built in bias. Some government sponsored ones have expensive or undemocratic outcomes. Some stifle debate e.g. "When I want your opinion, I'll give it to you". "I'm sure you'll agree", "You're either for me or against me."

One of the most-common types of bias is confirmation bias. This affects the way we test and evaluate daily news items. Confirmation bias is the tendency to seek out or interpret evidence in 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. Confirmation bias can lead us to make ill-informed choices or 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 can cause faulty conclusions based on inaccurate sample groups or data. Generally, the cause of sample bias is poor study design and data collection. As a result it is important to get a representative picture of the population. This can prove difficult if the people generating the study are prone to human flaws, including their own biases. A common example involves which political party is likely to win an election. If the study only polls college students the poll will not accurately reflect the opinions of the general population. Marketing surveys that only target the assumed buyers of a product can mean an entire segment of the potential marketplace gets ignored or is targeted profitably by a competitor. 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 one that makes us more likely to think of ability in some careers as a masculine trait and a female trait in some others. In fact, The Journal of Experimental Social Psychology study on brilliance bias suggests a likely source of this is the uneven distribution of men and women across careers typically associated with high intelligence or skill. This could of course be simply because fewer individuals of a particular group apply for these positions based on their own personal negative "brilliance bias" While this is a remnant of historical factors that limited access to education and career choices in the past, its presence has made us (wrongly) conclude that some types of individuals are less brilliant. Naturally, as the cycle perpetuates the uneven distribution in these careers, it only reinforces this bias. Ethnicity has had an obvious bias historically.