The Trouble with Data About Data
Two people looking at the same analytical result can come to different conclusions. The same goes for the collection of data and its presentation. A couple of experiences underscore how the data about data -- even from authoritative sources -- may not be as accurate as the people working on the project or the audience believe. You guessed it: Bias can turn a well-meaning, "objective" exercise into a subjective one. In my experience, the most nefarious thing about bias is the lack of awareness or acknowledgement of it.
The Trouble with Research
I can't speak for all types of research, but I'm very familiar with what happens in the high-tech industry. Some of it involves considerable primary and secondary research, and some of it involves one or the other.
Let's say we're doing research about analytics. The scope of our research will include a massive survey of a target audience (because higher numbers seem to indicate statistical significance). The target respondents will be a subset of subscribers to a mailing list or individuals chosen from multiple databases based on pre-defined criteria. Our errors here most likely will include sampling bias (a non-random sample) and selection bias (aka cherry-picking).
The survey respondents will receive a set of questions that someone has to define and structure. That someone may have a personal agenda (confirmation bias), may be privy to an employer's agenda (funding bias), and/or may choose a subset of the original questions (potentially selection bias).
The survey will be supplemented with interviews of analytics professionals who represent the audience we survey, demographically speaking. However, they will have certain unique attributes -- a high profile or they work for a high-profile company (selection bias).
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