Ambrosia is a Python library for A/B tests design, split and result measurement

Workshop on basic machine learning, computational modeling, psychophysics, basic data analysis and experiment design

ABacus: fast hypothesis testing and experiment design solution

Adaptive Design Optimization for Experimental Tasks

Optimal Bayesian Experiment Design

A list of resources for research scientists in psychology who use VR/AR/XR

Raspberry Pi Pico firmware for universal hardware control & measurement, along with a user-friendly Python frontend

A Python Library for Implementing Human-Computer Interface Experiments

🔨 Malet (Machine Learning Experiment Tool) is a tool for efficient machine learning experiment execution, logging, analysis, and plot making.

A Javascript library to conveniently add distribution builders to your online and offline experiments.

Design experiments distributed in several batches

A curated list of awesome resources for science and academia.

Run delayed and risky choice (DARC) experiments using Bayesian Adaptive Design

A walk through A/B Tests for new feature

PsyGlass: An open-source framework for implementing Google Glass in research settings

Python library for adaptive experiment design with state-of-art ML tools

Simulation tool for optimal design of high-dimensional MS-based proteomics experiment

An application to easily set up and run online listening experiments for music research.

El diseño de experimentos es uno de los pilares de la estadística y la ciencia de datos. En este tutorial te explic, que son, como se hacen y los conceptos clave como intervalos de confianzo, p-values, z-scores y más.

Frame-differencing method: Automatic extraction of movement from video data