README.md

metadata

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.

Task composition of P-Bench. The inner ring shows the data domains, and the outer ring shows the hypothesis-testing methods.

P-Bench was introduced in Fisher-R1: Training LLM Agents for Reliable Hypothesis Testing.

Quick start

Load the uniform task index:

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:

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:

from huggingface_hub import snapshot_download

root = snapshot_download(
    repo_id="May2222/P-Bench",
    repo_type="dataset",
    local_dir="pbench",
    token=True,
)

Dataset structure

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:

@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}
}