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Cognitive bias refers to systematic patterns of deviation from norm or rationality in judgment, where individuals create their own 'subjective reality' from their perception of the input. These biases often result from the brain's attempt to simplify information processing, leading to errors in decision-making and judgment.
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Confirmation bias is the tendency to search for, interpret, and remember information in a way that confirms one's preexisting beliefs or hypotheses. This cognitive bias can lead individuals to give more weight to evidence that supports their beliefs and undervalue evidence that contradicts them, thus reinforcing existing views and potentially leading to poor decision-making.
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Forensic evidence refers to information obtained through scientific methods that is used in legal proceedings to establish facts related to a crime. It encompasses a wide range of disciplines, including biology, chemistry, and digital analysis, to provide objective and reliable data that can corroborate or refute claims made during investigations and trials.
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Expert testimony is a legal mechanism where individuals with specialized knowledge, skills, or experience provide opinions or insights to assist the court in understanding complex matters beyond the average person's understanding. The credibility and admissibility of Expert testimony depend on the expert's qualifications, the relevance of their testimony, and adherence to legal standards such as the Daubert or Frye standards.
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Standardization is the process of developing and implementing technical standards to ensure consistency and compatibility across different systems or products. It facilitates interoperability, improves quality, and reduces costs by minimizing variations and fostering uniformity in processes and outputs.
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Validation is the process of ensuring that a system or component meets its intended requirements and functions correctly in its operational environment. It is a critical step in quality assurance, providing confidence that the product or service will perform as expected in real-world scenarios.
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Error rate is a measure of the frequency of errors in a process or system, often expressed as a percentage or ratio. It is crucial for evaluating the performance and reliability of models, especially in fields like machine learning and quality control, where minimizing errors is essential for accuracy and efficiency.
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Reliability refers to the consistency and dependability of a system, process, or measurement over time. It is crucial for ensuring trust and accuracy in various fields, such as engineering, psychology, and statistics, where repeated results are essential for validation and decision-making.
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Human error refers to mistakes made by individuals, often due to cognitive biases, lack of knowledge, or environmental factors, which can lead to unintended outcomes. Understanding and mitigating Human error is crucial in designing systems and processes that enhance safety and efficiency.
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Bias mitigation involves strategies and techniques to reduce or eliminate bias in data-driven systems, ensuring fairness and equity in decision-making processes. It is crucial for building trust in AI systems and preventing discrimination against marginalized groups.
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Bite mark analysis is a forensic technique used to identify individuals based on the unique characteristics of bite marks left on a victim or object. However, it has faced significant criticism and skepticism due to its subjective nature and lack of scientific reliability, leading to wrongful convictions in some cases.
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