In academic research, improper manipulation of data to obtain false research results is a serious academic misconduct that violates the basic ethics and principles of scientific research. Understanding these misconducts will help maintain academic integrity and improve research quality. The following are some common ways to improperly manipulate data, but please note that these behaviors are unacceptable: 1. **Data falsification**: Creating non-existent data or modifying original data to make it conform to the expected results. 2. **Selective data reporting (P-hacking)**: Selectively reporting data that meets the research hypothesis from a large amount of collected data, while ignoring those that do not. 3. **Data modification**: Slightly modifying data to achieve the expected results by changing the standard or scale of data recording. 4. **Repeated publication of data**: Splitting or changing the description method of the same set of data to publish multiple articles to increase the number of publications. 5. **Ignoring outliers**: Deliberately ignoring or deleting outliers that are unfavorable to the hypothesis, rather than explaining and processing these data in detail. 6. **Improper statistical treatment**: Selecting or using improper statistical analysis methods to obtain results that support the research hypothesis. 7. **Misleading data visualization**: Selectively constructing charts, images, tables, etc. to mislead data or results. 8. **Concealing negative results**: Deliberately not reporting negative results or failed experiments in research. These behaviors not only damage the credibility of the academic community, but also have a negative impact on the entire society and public trust. Therefore, researchers should follow the highest standards of academic integrity and conduct transparent, reproducible and reliable research.