The purpose of collecting and analyzing data is of course to use it, but before data can be used, it is necessary to interpret the data and draw conclusions based on the study conducted. Many things influence how data is interpreted and by extension, how changes made based on the data is, or isn’t, implemented.
Bias is one of the ways in which data might be influenced in a way that can change its interpretation. Bias occurs when the results of a study are in some way distorted based on a variable that isn’t accounted for in the tests. Often, bias comes from the ones conducting the tests, and thus, the results can reflect the personal beliefs of the ones performing a test. For example, an evaluator may ignore certain problems that they themselves deem unimportant, or unknowingly influence an interviewee to respond in a certain way based on the phrasing of questions or the tone of voice in which the questions are delivered. In that way, Bias can not only effect how one interprets data, but can also effect what data is gathered in the first place.

There are, of course, other influences on how data is gathered and interpreted, but I believe bias is one of the biggest and most harmful influences. Eliminating bias in studies is essential to ensuring the integrity of the data one collects, and ensuring that the data decisions are based on is as accurate as possible, so that changes made to the product are effective and and will improve the functionality and usability of the product for all intended users, rather than missing changes the product might need, or including things that make the product harder to use for a portion of users.
References
Preece, Rogers, & Sharp, (2015). Interaction Design: Beyond Human Computer Interaction. West Sussex, United Kingdom: John Wiley & Sons, Ltd.








