---
esco_uri: http://data.europa.eu/esco/skill/f6ac6241-adb2-4ce2-b35f-09832cbb11ea
esco_label: "empirical analysis"
relation: essential
onet_soc: 15-2051.00
source: model-knowledge
confidence: high
qa_count: 0
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# empirical analysis — Data Scientist

For a Data Scientist, 'empirical analysis' isn’t just *doing* statistics; it's the core of turning raw data into actionable insights. Daily work revolves around formulating hypotheses – “will this marketing campaign increase conversions?”, “are these customer segments truly distinct?” – and then rigorously testing them against real-world data. This means going beyond descriptive stats to employ inferential techniques (A/B testing, regression analysis, causal inference) to *prove* or disprove those assumptions. It's about letting the data speak, even if it contradicts initial beliefs.

Typical tasks involve extensive exploratory data analysis (EDA) using tools like Python (Pandas, NumPy, Scikit-learn), R, or SQL to understand data distributions and identify potential relationships. Then comes model building – not just fitting algorithms, but critically evaluating their performance *on unseen data* via techniques like cross-validation.  A huge part is also data cleaning & preprocessing; garbage in, garbage out applies massively here. Good empirical analysis isn't about finding a 'significant' result, it’s about understanding the limitations of that significance and potential biases.

A common pitfall is ‘p-hacking’ – manipulating analyses until you get the desired statistical significance without genuine evidence. Another is confusing correlation with causation; just because two variables move together doesn’t mean one causes the other.  Data Scientists need to be skeptical, document their process meticulously (reproducibility!), and understand the assumptions underlying each analytical method. Version control (Git) for code & data pipelines are essential.

What 'good' looks like is a clear, well-documented analysis that explicitly addresses uncertainty. It’s presenting findings not just as numbers, but with confidence intervals, effect sizes, and a discussion of potential confounding factors.  It means being able to explain complex results to non-technical stakeholders in a way they can understand the implications for business decisions – and acknowledging when the data *doesn't* provide a clear answer.

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).
