data-analysis

Data analysis findings are verified through re-computation and hypothesis testing, ensuring every insight is backed by reproducible numbers. This iterative process validates effect sizes and maintains a report of substantiated results.

Iterative exploratory data analysis is a self-checking process for surfacing verified patterns within datasets. You need it when exploring large datasets where every discovered insight must be substantiated with numeric evidence.

Start hypothesis testing by configuring a loop.run.yaml file with your dataset, then execute loop.run. This initiates the iterative analysis process, proposing specific hypotheses and verifying them through data re-computation.

Yes, this data analysis approach is explicitly applicable to large datasets. It emphasizes reproducible research and iterative hypothesis testing, enabling data analysts to explore and discover meaningful patterns with validated numeric evidence.

Reproducible research scripting requires Python 3.9 or higher. This environment dependency ensures compatibility for running the iterative data analysis loops, hypothesis testing, and re-computation validation processes.

The best way to validate effect sizes is through data re-computation during iterative hypothesis testing. This self-checking mechanism verifies proposed hypotheses and ensures findings are backed by reproducible data before reporting.