Technical analysis relies on the study of historical market data to identify patterns that may forecast future price movements. While this approach can provide valuable insights, it is highly susceptible to distortion from cognitive and methodological biases if not properly addressed.
The term bias generally refers to any prejudice or propensity that skews the results or inferences of a research process. In the context of technical analysis, biases arise whenever systematic errors are introduced into how data is gathered, interpreted, or used to form conclusions. Even subtle biases can seriously undermine the validity and reliability of analytical findings if left unexamined.
There are four primary types of bias that technical analysts should be aware of:
- Confirmation bias - The tendency to search for or favor information that supports existing preconceptions over contradictory evidence.
- Selection bias - Distortions that occur when non-random data sampling methods are used, resulting in data that is not truly representative.
- Survivorship bias - A form of selection bias where outcomes are analyzed only for entities that "survived" a process, ignoring failures.
- Information bias - Errors introduced during data collection that threaten internal validity, such as inconsistent measurement techniques over time.
Understanding these common sources of innate cognitive and methodological distortion is crucial for conducting sound technical analysis. In future posts, we will explore principles for mitigating bias risks, examine implicit biases embedded within core analytical frameworks, and discuss practical steps for ongoing debiasing efforts. Awareness of potential biases serves both experienced and novice chart analysts.
In Part 2, we will outline foundational methodological principles that can help minimize analytical distortions. Please let me know if any part of the series would benefit from further expansion or explanation.
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