Overview
This guide addresses common biases affecting product research and experiments within GLIDR. There are many biases you should watch out for so you don't skew your results.
Cognitive Biases
The article identifies 15 cognitive biases, including:
- Anchoring Effect: Using initial information to shape later judgments
- Availability Bias: Assessing likelihood based on how readily examples come to mind
- Confirmation Bias: Favoring information supporting existing beliefs
- Curse of Knowledge: Difficulty empathizing due to superior subject knowledge
- Halo Effect: Letting positive impressions obscure negative aspects
- Hindsight Bias: Falsely believing past outcomes were predictable
- Observer Bias: Influencing research through knowledge of study objectives
- Overconfidence: Overestimating personal abilities and underestimating personal risk
- Primacy/Recency Effects: Overweighting initial or final information
- Self-Fulfilling Prophecy: Expectations shaping observed behavior
Research Biases
Seven additional research-specific biases are covered:
- Selection Bias
- Measurement Bias
- Framing Effect
- False Positives/Negatives
- Omitted-Variable Bias
- Planning Effect
The article emphasizes consulting individual method sections for bias details and provides extensive external references for deeper learning.