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experiment

What It Means

An experiment is a controlled test where you deliberately change one thing to see how it affects something else, while keeping everything else the same. In AI contexts, this means systematically testing different approaches, parameters, or data inputs to understand what drives better model performance or business outcomes.

Why Chief AI Officers Care

Proper experimentation is essential for proving AI ROI, optimizing model performance, and making data-driven decisions about AI investments. Without controlled experiments, CAIOs cannot reliably distinguish between genuine AI improvements and random noise, leading to poor resource allocation and failed AI initiatives.

Real-World Example

A CAIO testing whether a new recommendation algorithm increases sales by randomly showing the new algorithm to 50% of website visitors and the old algorithm to the other 50%, then measuring conversion rates over 30 days while keeping all other website features identical.

Common Confusion

Many organizations confuse simple A/B testing or pilot programs with proper experiments, missing the critical requirements of random assignment and controlling for other variables. This leads to drawing false conclusions about what actually caused observed changes in performance.

Industry-Specific Applications

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Healthcare: In healthcare AI, experiments involve systematically testing different model architectures, training datasets, or clinic...

Finance: In finance, experiments involve systematically testing AI models, trading strategies, or risk management approaches usin...

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Technical Definitions

NISTNational Institute of Standards and Technology
"a series of observations conducted under controlled conditions to study a relationship with the purpose of drawing causal inferences about that relationship. An experiment involves the manipulation of an independent variable, the measurement of a dependent variable, and the exposure of various participants to one or more of the conditions being studied. Random selection of participants and their random assignment to conditions also are necessary in experiments. "
Source: apa_experiment_2023
"A study of a fundamental physical process by the use of one or more computer simulators. Like empirical experiments, input variables (factors) are systematically changed to assess their impact upon simulator outputs (responses). Unlike empirical experiments, the simulator responses are deterministic, and this has implications: Computer experiments can appropriately have their factors with intermediate levels and the scope, especially the number of runs, can be more ambitious. Further, modeling methods based on interpolators (especially kriging) emerge as a viable approach. Good practice is to use Latin hypercubes for computer experiments, and advanced nonparametric modeling methods such as kriging, neural networks, and multivariate adaptive regression splines (MARS) in the data analysis stage. Important applications of computer experimental methods are for determining process optima and for evaluating process tolerances."
Source: nist_statistics_2012

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