Misleading data analysis is often used to support false business decisions in the following situations: 1. **Selective data**: Only select data that is favorable to oneself and ignore contradictory evidence. For example, a company may only show the successful sales data of a product in a certain region, while ignoring the low sales performance in other regions. 2. **Data manipulation**: Improper processing of data, such as tampering with data, deleting outliers, or using unreasonable filtering conditions, so that the analysis results appear to be in line with expectations. 3. **Absolute and relative data confusion**: Confusing absolute values with proportions, giving a seemingly satisfactory growth percentage while ignoring the change in the base. For example, a company's revenue increased by 200%, but if the base is very small, the actual amount may be negligible. 4. **Inappropriate comparison**: Comparing unrelated or dissimilar data to draw misleading conclusions. For example, comparing sales performance in different market environments may lead to inaccurate business decisions. 5. **Over-complication**: Using complex statistical models or data visualization techniques to conceal the true situation behind the data, making it difficult for non-professionals to understand and question the analysis results. 6. Confirmation bias: Analysts may consciously or unconsciously choose data and methods that only support their existing views, resulting in biased results. 7. Lack of data context: Not providing enough background information or data sources, making it difficult for decision makers to fully understand the actual situation behind the data. In these cases, misleading data analysis may lead companies to make wrong strategic decisions, resulting in financial losses or damaged reputation. To avoid these problems, companies need to establish transparent and responsible data analysis processes and conduct strict review of data sources and methods.