Experimentation is ubiquitous in the field of psychology and fundamental to the
advancement of its science, and one of the biggest challenges for researchers is designing …

Experimentation is at the core of research in the behavioral and neural sciences, yet
observations can be expensive and time-consuming to acquire (eg, MRI scans, responses …

Suppose an online platform wants to compare a treatment and control policy (eg, two
different matching algorithms in a ridesharing system, or two different inventory management …

Adaptive designs allow planned modifications based on data accumulating within a study.
The promise of greater flexibility and efficiency stimulates increasing interest in adaptive …

Adaptive design refers to experimental design where the next sample point is determined by
information from previous experiments. This article presents a constrained optimization …

The characteristics of the stimuli used in an experiment critically determine the theoretical
questions the experiment can address. Yet there is relatively little methodological support for …

Bayesian optimisation is a statistical method that efficiently models and optimises expensive
“black-box” functions. This review considers the application of Bayesian optimisation to …

To maximize the chance for success in an experiment, good experimental design is needed.
However, the presence of unique constraints may prevent mapping the experimental …

Discriminating among competing statistical models is a pressing issue for many
experimentalists in the field of cognitive science. Resolving this issue begins with designing …

Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …