> ## Documentation Index
> Fetch the complete documentation index at: https://sourcetable.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# For scientific research

> Experimental data processing, statistical analysis, and reproducible research workflows.

## Example workflows

| Task | How to do it |
| - | - |
| Experimental analysis | "Run a paired t-test comparing pre and post treatment measurements" |
| Data visualization | "Create a scatter plot with regression line and 95% confidence bands" |
| Literature data | Use deep research to "Find recent studies on \[topic] and summarize key findings" |
| Power analysis | "Calculate the required sample size for detecting a medium effect with 80% power" |
| Meta-analysis | "Calculate pooled effect sizes from these study results using random effects model" |
| Data cleaning | "Standardize units, handle missing values, and flag impossible measurements" |

## Key features for research

* **[Statistical analysis](https://sourcetable.com/docs/data-science/statistics.md)** — t-tests, ANOVA, chi-square, regression, non-parametric tests
* **[Feature engineering](https://sourcetable.com/docs/data-science/feature-engineering.md)** — Data transformation and normalization
* **[Deep research](https://sourcetable.com/docs/tools/deep-research.md)** — Literature search and synthesis
* **[Python](https://sourcetable.com/docs/tools/python.md)** — Full access to SciPy, StatsModels, scikit-learn
* **[Import/export](https://sourcetable.com/docs/data/import-export.md)** — Handle CSV, XLSX, JSON, Parquet, and more


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