# The Power of Batching in Multiple Hypothesis Testing

arXiv: https://arxiv.org/abs/1910.04968 · HTML: https://mlsanity.com/paper/1910.04968/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

## Built on by (most cited first, 4 of 4)

- [Online false discovery rate control for anomaly detection in time series](https://mlsanity.com/paper/2112.03196/) (2021)
- [Online multiple testing with e-values](https://mlsanity.com/paper/2311.06412/) (2023)
- [Online Control of the False Discovery Rate under "Decision Deadlines"](https://mlsanity.com/paper/2110.01583/) (2021)
- [Online False Discovery Rate Control for LORD & SAFFRON Under Positive, Local Dependence](https://mlsanity.com/paper/2110.08161/) (2021)

## Abstract (arXiv)

One important partition of algorithms for controlling the false discovery rate (FDR) in multiple testing is into offline and online algorithms. The first generally achieve significantly higher power of discovery, while the latter allow making decisions sequentially as well as adaptively formulating hypotheses based on past observations. Using existing methodology, it is unclear how one could trade off the benefits of these two broad families of algorithms, all the while preserving their formal FDR guarantees. To this end, we introduce BatchBH and BatchSt-BH, algorithms for controlling the FDR when a possibly infinite sequence of batches of hypotheses is tested by repeated application of one of the most widely used offline algorithms, the Benjamini-Hochberg (BH) method or Storey's improvement of the BH method. We show that our algorithms interpolate between existing online and offline methodology, thus trading off the best of both worlds.