🔭 SCIENTIFIC RESEARCH

Reproducible, Bias-Free Experiments

ZPL provides a mathematical null hypothesis baseline for computational science. Same input, same output — always. Every result has an AIN neutrality certificate.

Reproducibility Demo API Reference

Live Demo

Deterministic Reproducibility

Run the same ZPL computation multiple times. The sequence and AIN score are always identical — essential for peer review and replication.

Input: bias=0.3, N=9, samples=1000

Click "Run Again" to verify reproducibility

Why Reproducibility Matters

Replication crisis — over 50% of published psychology and biology studies fail to replicate, partly due to random seed issues

Random seed dependency — ML experiments with different seeds give vastly different results, making comparison impossible

No baseline — without a mathematical null hypothesis, it's hard to prove an effect is real vs random noise

ZPL as Scientific Standard

Deterministic equilibrium — ZPL output at bias=0.5 is the mathematical definition of no effect

Deviation = signal — any AIN score below 1.0 quantifies exactly how far from null hypothesis the data is

Independent verification — any lab can run the same ZPL parameters and get the same result

Applications

ZPL Across Research Domains

🧪

A/B Testing

Statistically fair group assignment. ZPL ensures control and treatment groups are balanced without depending on researcher choices.

Unbiased assignment

📐

Null Hypothesis Baseline

ZPL equilibrium (AIN=1.0, bias_output=0.5) is a mathematically defined null. Compare your results against this baseline.

Mathematical H₀

👥

Peer Review Assignment

Assign reviewers to papers using ZPL randomness. Prevents systematic bias in which reviewers see which papers.

Fair assignment

🧬

Clinical Trials

Randomized controlled trials need provably fair group assignment. ZPL provides an auditable trail for every patient allocation.

Auditable RCT

🤖

ML Experiment Baselines

Use ZPL-balanced datasets as the canonical baseline across experiments. Eliminates seed variance as a confound.

Seed-independent

📊

Survey Randomization

Randomize question order and participant assignment in surveys without introducing systematic bias from the randomization itself.

Order-bias free

Code

Python Research Integration

# Python — ZPL as null hypothesis baseline for experiments import requests import numpy as np ZPL_KEY = "zpl_your_key" def zpl_null_hypothesis(n: int = 9, samples: int = 1000) -> dict: """Get ZPL equilibrium baseline — the mathematical null hypothesis.""" res = requests.post( "https://zpl-backend.onrender.com/compute", headers={"X-Api-Key": ZPL_KEY}, json={"bias": 0.5, "N": n, "samples": samples} ).json() return {"ain": res["ain_score"], "sequence": res["sequence"]} def zpl_ab_split(n_subjects: int) -> dict: """Split subjects into A/B groups using ZPL balanced randomness.""" assignments = [] for i in range(0, n_subjects, 9): res = requests.post( "https://zpl-backend.onrender.com/compute", headers={"X-Api-Key": ZPL_KEY}, json={"bias": np.random.random(), "N": 9, "samples": 500} ).json() assignments.extend(res["sequence"]) assignments = assignments[:n_subjects] return { "group_A": [i for i,v in enumerate(assignments) if v == 0], "group_B": [i for i,v in enumerate(assignments) if v == 1], "balance": sum(assignments) / len(assignments) # always ~0.5 }

PRICING

Simple, Transparent Pricing

Start free. Academic discounts available — contact us.

PRO

$29/month

35,000 compute calls/month

✓ Full reproducibility audit

✓ GET /stability report

✓ 3 API keys