Misrepresentation of data involves presenting data in a misleading way to distort the truth, often to influence opinions or decisions. This unethical practice can occur through selective reporting, manipulating visualizations, or omitting context, leading to false conclusions and undermining trust in data-driven insights.
Misleading graphs are visual representations of data that are intentionally or unintentionally designed to distort the truth, leading to incorrect interpretations. They can manipulate viewers' perceptions through techniques such as inappropriate scaling, truncated axes, or selective data omission, which can significantly impact decision-making and public opinion.
Contextual integrity is a privacy framework that emphasizes the importance of context in determining the appropriateness of information sharing and use. It suggests that privacy norms are context-dependent, and violations occur when information flows deviate from the expected norms of a given context.
Misleading statistics occur when data is presented in a way that distorts the truth, often to manipulate public perception or support a specific agenda. This can involve cherry-picking data, using inappropriate scales, or omitting relevant context, leading to incorrect conclusions.