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