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license: cc-by-4.0
language:
- en
pretty_name: P-Bench
size_categories:
- n<1K
tags:
- benchmark
- statistics
- statistical-analysis
- tabular
- tool-use
- r
configs:
- config_name: default
default: true
data_files:
- split: benchmark
path: data/manifest.parquet
---
# P-Bench
P-Bench is a benchmark for evaluating statistical analysis agents on realistic,
open-ended hypothesis-testing problems. It contains **425 tasks** spanning
economics, biology, and medicine across **17 statistical test families**. Given
only a scientific hypothesis and a dataset, an agent must select an appropriate
method, compute a p-value, and draw a conclusion.
<p align="center">
<img src="assets/balanced_pie_chart.png" alt="Composition of P-Bench by data domain and hypothesis-testing method" width="900">
</p>
<p align="center"><em>Task composition of P-Bench. The inner ring shows the data domains, and the outer ring shows the hypothesis-testing methods.</em></p>
P-Bench was introduced in
[*Fisher-R1: Training LLM Agents for Reliable Hypothesis Testing*](https://arxiv.org/abs/2608.07437).
## Quick start
Load the uniform task index:
```python
from datasets import load_dataset
tasks = load_dataset(
"May2222/P-Bench",
split="benchmark",
token=True,
)
print(tasks)
print(tasks[0]["task_id"], tasks[0]["dataset_path"])
```
Load the heterogeneous CSV associated with one task:
```python
import pandas as pd
from huggingface_hub import hf_hub_download
task = tasks[0]
csv_path = hf_hub_download(
repo_id="May2222/P-Bench",
repo_type="dataset",
filename=task["dataset_path"],
token=True,
)
data = pd.read_csv(csv_path)
```
Download the complete benchmark in its evaluator-compatible directory layout:
```python
from huggingface_hub import snapshot_download
root = snapshot_download(
repo_id="May2222/P-Bench",
repo_type="dataset",
local_dir="pbench",
token=True,
)
```
## Dataset structure
```text
P-Bench/
├── README.md
├── splits.json
├── data/
│ └── manifest.parquet
└── tasks/
└── <category>/
└── <task_name>/
├── question.txt
├── dataset.csv
└── answer_key.json
```
Each task directory is self-contained:
- `question.txt` describes the research question, hypotheses, study design, and
variable glossary available to the agent.
- `dataset.csv` is the task's input table.
- `answer_key.json` contains `p_value` and `decision`, where `decision` is
`reject` or `fail_to_reject` at alpha = 0.05.
- `splits.json` maps every `<category>/<task_name>` identifier to the `Easy` or
`Hard` difficulty partition.
### Manifest
Each row in `data/manifest.parquet` represents one task and includes its
question, difficulty, source metadata, reference answer, file paths, table
dimensions, and checksum.
## Tasks by encoded source prefix
| Source prefix | Tasks | Description |
|---|---:|---|
| `dfeep` | 219 | Economic, policy, survey, and field-experiment task family, including derived variants. |
| `biodsa` | 173 | Biomedical and cancer-genomics task family; many questions identify a cBioPortal study. |
| `synth` | 20 | Explicitly synthetic task tables. |
| `hbiostat` | 7 | Biostatistics teaching/reference datasets identified in the question text. |
| `medical_bio` | 6 | Medical and biostatistical benchmark datasets. |
## Intended uses
P-Bench is intended for:
- evaluating statistical analysis agents and tool-using language models;
- studying method selection, statistical coding, numerical reporting, and
hypothesis-test interpretation;
- comparing agent robustness across statistical families and difficulty
partitions.
P-Bench is not intended to provide clinical, medical, legal, financial, or
public-policy advice. A correct benchmark answer does not establish that an
agent is safe to deploy without expert review.
## Citation
If you use P-Bench, please cite the associated paper:
```bibtex
@article{miao2026fisherr1,
title = {Fisher-R1: Training LLM Agents for Reliable Hypothesis Testing},
author = {Miao, Jiacheng and Mu, Jin and Chen, Guanhua and Zou, James},
journal = {arXiv preprint arXiv:2608.07437},
year = {2026},
url = {https://arxiv.org/abs/2608.07437}
}
```
|