The Unrealistic Expectations Problem: How Modern Labs Demand Expertise They Never Train

A silent but pervasive contradiction shapes modern biomedical research: the expectation that every scientist should somehow be a biologist, a statistician, a data engineer, and a machine-learning practitioner all at once.

This expectation appears subtle, but it is everywhere:

• Graduate students are told to “analyze the dataset” without training in statistical principles beyond introductory coursework.
• Postdocs are instructed to “run the machine learning model” without knowing how the model works, what assumptions it makes, or how the codebase was built.
• Wet-lab researchers are handed thousands of files and expected to write scripts to process them, even though no one ever taught them version control, data structures, environment management, or algorithmic thinking.

The assumption is that because a tool exists—R, Python, scikit-learn, TensorFlow—anyone can simply pick it up and absorb it with minimal overhead. In reality, learning computational science requires hundreds of hours of focused practice. It is not a quick add-on to an already full experimental schedule.

Statisticians could guide the process, but in most labs they are in high demand and chronically understaffed. A single statistician may serve dozens of researchers, none of whom have the continuity or mentorship needed to deeply learn the tools being used. When the statistician steps away, the students are left holding fragile scripts they do not fully understand.

This creates a research culture where people are embarrassed to admit their confusion, where analyses become black boxes held together by copy-pasted code, and where faculty often lack visibility into the complexity of the tasks they assign.

The result is predictable:
Unrealistic demands produce shallow understanding, inconsistent analyses, and avoidable mistakes—none of which reflect a lack of intelligence, only a lack of structural support.

If modern labs expect computational rigor, they must provide pathways for it—through accessible tools, better interfaces, integrated AI assistance, and explicit recognition of the true load placed on non-programmer scientists.