My most common advice for junior researchers

2026-04-02 · Source: AI Alignment Forum · Field: Science & Research — Research Methodology & Innovation, Artificial Intelligence & Machine Learning Research · Depth: Intermediate, medium

Summary

This article, part of the Inkhaven Fellowship, focuses on the critical research practice of performing "quick sanity checks." It highlights how researchers often waste significant time on fruitless investigations that could be avoided by basic checks. Examples include assessing data for bias, verifying theorem non-triviality, checking correlations in data analysis, and quantitatively understanding data dimensions like tool call success rates in LLM agents or reasoning chain lengths. The author also suggests creating small, concrete examples to debug algorithms or validate theoretical claims, such as testing if a similarity measure satisfies distance metric properties. Crucially, the article emphasizes that these checks must be "quick," prioritizing speed over exhaustive rigor to avoid premature, time-consuming efforts like building extensive data processing pipelines.

Key takeaway

For research scientists developing new models or analyzing complex datasets, integrating quick sanity checks into your workflow is crucial. Before deep dives, verify your data for obvious biases, test core assumptions with small examples, and quantitatively survey key data dimensions. This approach will help you rapidly identify and correct fundamental issues, preventing significant time investment in flawed directions and accelerating your research progress.

Key insights

Quick sanity checks prevent wasted research time by identifying fundamental flaws early and efficiently.

Principles

• Prioritize speed over rigor for initial checks.
• Quantitatively understand high-level data characteristics.
• Use small, concrete examples to validate algorithms or theories.

Method

Perform basic checks on ideas, data, and theorems; analyze key variable correlations and summary statistics; create small, concrete examples to test algorithms or theoretical claims.

In practice

• Check for basic correlations in data analysis.
• Calculate mean and standard deviation of key statistics.
• Walk through algorithms with small, concrete examples.

Topics

• Junior Research Advice
• Sanity Checks
• Data Analysis
• Algorithm Debugging
• Theoretical Research

Best for: AI Scientist, Research Scientist, AI Student

Related on AIssential

• Do a sanity check on your experiments — Manual sanity checks on data, model outputs, and evaluations are crucial for detecting common experimental bugs.
• Statistical analysis recapitulates the development of statistical methods — Statistical analysis often recapitulates the historical development of methods, progressing from simple techniques to complex models incrementally.
• Calmcode, Explosion, Data Science — Effective data science prioritizes problem rephrasing, system thinking, and community engagement over solely optimizing algorithms.
• Intelligence isn’t about parameter count. It’s about time. — Minimizing inference time in AI models fosters reasoning and algorithmic learning, counteracting the "savant regime" of brute-force scaling.
• Group-by statements that save the day — Simple data exploration, like GROUP BY, often reveals critical issues that complex ML models miss, emphasizing human judgment.
• A Scalability Analysis of Quantitative Confidence Assessment Methods for Assurance Cases — A numerical model can estimate the decision complexity and effort of quantitative confidence assessment methods for assurance cases.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by AI Alignment Forum.