When running Research and Experiments in GLIDR, there are many Biases you should watch out for so you don't skew your results. This article lists out many of them, plus each method has an additional section detailing common biases. Check out the Index of Methods page to see individual methods.
Article excerpted from The Real Startup Book
Anchoring Effect: Basing subsequent judgements of an event based on the event’s first piece of information.
Availability Bias: Judging an event because of how easy you can think of examples of the event.
Central Tendency: Categorizing and judging information as it relates to a prototype while ignoring variation.
Confirmation Bias: Seeking information and evidence that supports your beliefs and hypotheses, while ignoring conflicting information.
Curse of Knowledge: Lacking empathy towards others because of you know more than another person about a particular subject.
Fundamental Attribution Error: Thinking that people behave a certain way due to their personality and not the situation in which they find themselves in.
Halo Effect: Overvaluing the overall, good impression of a person, brand, product, organization, etc., which makes it easier to overlook any bad impression.
Hindsight Bias: Thinking that you knew-it-all-along ; however, prior to the event happening, you had no basis for your prediction.
Observer Bias: Influencing the research because, you, as the observer know of the study’s goals and objectives.
Overconfidence: A belief that you’re better than others, and that negative outcomes can happen to others (but not me).
Primacy Effect: Recalling and emphasizing information that happened towards the beginning of an experience.
Recall Bias: An incomplete and inaccurate remembering of past events or experiences.
Recency Effect: Recalling and emphasizing information that happened towards the end of an experience.
Response Bias: Range of biases that influences subject’s responses away from a truthful response.
Self-Fulfilling Prophecy: Expecting others to behave a certain way. Seeing the other person’s actions confirms your expectations.
Framing Effect: Seeing differences in experimental results because of differing contexts and situations, apart from experimental variables.
False Negative: A result that appears negative when it should not.
False Positive: A result that appears positive when it should not.
Measurement Bias: Systematic error in measurement or classification for participants in study.
Omitted-Variable Bias : Leaving out one or more causal variables in a statistical model.
Planning Effect: A belief that you’ve accurately estimated the planned work.
Selection Bias: Erroneously choosing participants to study, which affect your study results.
References (external links)
GLIDR allows you to link your experimentation and evidence with the Ideas on your roadmap.