In academic research, manipulating data to obtain false results is a serious violation of academic integrity that can lead to the retraction of papers, damage to reputation, and financial consequences. Here are some common strategies for improperly manipulating data to obtain false results: 1. **Selective presentation of data**: Selectively presenting part of the data or results, rather than the entire data set, to support the intended conclusion. 2. **Data falsification**: Completely fabricating data, including experimental, survey, or other data. 3. **Data tampering**: Modifying real data by: * Removing or adding data points * Changing data values or classifications * Manipulating measurement tools or devices 4. **Tidak representative sampling**: Deliberately selecting biased samples, including: * Non-random sampling * Oversampling or undersampling specific subpopulations * Using participants that may lead to biased results 5. **Talmudic data analysis**: Repeatedly performing statistical analyses until the desired results are obtained, and then removing analyses that did not obtain significant results. 6. **Reuse and republication**: Reusing the same data or results across multiple publications without proper citation or disclosure. 7. **Concealing methods of data collection or analysis**: Deliberately concealing methods of data collection, processing, or analysis to avoid scrutiny or criticism. 8. **Graph manipulation**: Manipulating a chart or graph to support a false conclusion, including: * Scaling or enlarging axes* Removing or changing labels or axis labels* To drown out or misrepresent data 9. **Reporting incorrect statistics**: Incorrectly presenting statistical results, including: * Calculating the wrong p-value* Using inappropriate statistical tests* Concealing assumptions of statistical analyses 10. **Peer review obstruction**: Obstructing the peer review or review process, including: * Deliberately avoiding review* Submitting incomplete or misleading data sets* Making personal attacks in response to reviews or criticisms 11. **Backtracking**: Searching for patterns or correlations in data and then treating those findings as a priori hypotheses. 12. **Using inappropriate data processing techniques**: Using inappropriate data processing techniques, such as: * Using inappropriate normalization methods * Ignoring data quality issues * Using data processing techniques that do not address the research question 13. **Concealing data sources**: Deliberately concealing data sources, including: * Not disclosing the period when data were collected * Not providing information about the source of the data * Concealing potential bias in data collection or analysis 14. **Creating biased questionnaires or surveys**: Designing biased surveys or questionnaires, including: * Using biased language * Including problematic or misleading questions * Highlighting or emphasizing certain answer options 15. **Exploiting researcher bias**: Allowing personal biases or expectations to influence data analysis or interpretation, including: * Interpretations driven by personal values or beliefs that may affect interpretations * Ignoring evidence that contradicts previous findings To avoid these strategies and maintain the integrity of the research, researchers should: * Follow honest research practices * Make methods and data publicly available * Participate in an open and transparent peer review process * Recruit a diverse research team for data collection and analysis * Participate in ongoing?ào t?o and employment development to maintain expertise and skills* Prioritize honesty and fairness over expected results or expectationsNúmero