Final answer:
In the event of assumption violations in statistical analysis, robustness checks are essential, such as using alternatives to parametric tests, sensitivity analyses, re-evaluating sample size, consulting literature or professionals, and being aware of statistical fraud and errors to maintain credible research.
Step-by-step explanation:
When you find a violation of assumptions in statistical analysis, you should perform robustness checks to see if your statistical conclusions are still valid. This might include using different statistical tests that do not require the violated assumption or using bootstrapping methods to assess the impact of the violation.
For example, if you find that the assumption of normal distribution is violated, you might use non-parametric tests instead of parametric tests. Additionally, consider increasing the sample size to help mitigate the impact of violations as larger sample sizes can reduce the risk of Type I and Type II errors. It's essential to have an understanding of basic statistical procedures to recognize proper data analysis and ensure an ethical approach in statistical studies.
Key steps to follow when you suspect a violation might be:
- Assess the violation and its potential impact on your analyses.
- Consider alternative statistical methods or tests that are less sensitive to the assumptions.
- Conduct sensitivity analyses to determine if your conclusions hold under different conditions or assumptions.
- Re-evaluate your sample size and the power of your study, as appropriate changes might be necessary to improve accuracy.
- Consult statistical literature or a professional statistician for guidance on dealing with the specific violation.
Understanding these considerations will prepare you to avoid the pitfalls of statistical fraud and errors, ensuring that your research maintains a high standard of integrity and credibility.