Despite their benefits, there are several limitations and challenges associated with these tools:
Complexity: Some tools, particularly programming languages like R, have a steep learning curve. Data Quality: The accuracy of analyses and models is contingent on the quality of the input data. Cost: Proprietary software like SAS and Stata can be expensive, potentially limiting access. Interoperability: Ensuring compatibility and seamless integration between different tools can be challenging.
