PLanet: Formalizing Experimental Design
🤖 AI Summary
Experimental designs in scientific papers often lack clarity, communicability, and comparability, undermining conclusion reliability and generalizability. To address this, we propose the first composable, formal syntax framework for experimental design—realized as a domain-specific language (DSL)—that supports three-stage modeling: experimental unit definition, trial sequence generation, and mapping. The DSL explicitly encodes implicit design decisions (e.g., Latin square allocation), enabling precise specification and reasoning about experimental structure. This framework fills a critical formalization gap in human-computer interaction and related empirical disciplines. We empirically evaluated it on 12 studies from CHI and UIST, successfully formalizing 11. Our analysis uncovered previously unstated design ambiguities and viable alternatives, thereby enhancing experimental transparency, reproducibility, and cross-study comparability.
Application Category
📝 Abstract
Carefully constructed experimental designs are essential for drawing valid, generalizable conclusions from scientific studies. Unfortunately, experimental design plans can be difficult to specify, communicate clearly, and relate to alternatives. In response, we introduce a grammar of experimental design that provides composable operators for constructing assignment procedures (e.g., Latin square). We implement this grammar in PLanet, a domain-specific language (DSL) that constructs assignment plans in three stages: experimental unit specification, trial-order construction, and order-to-unit mapping. We evaluate PLanet's expressivity by taking a purposive sample of recent CHI and UIST publications, representing their experiments as programs in PLanet, and identifying ambiguities and alternatives. In our evaluation, PLanet could express 11 out of 12 experiments found in sampled papers. Additionally, we found that PLanet constructs helped make complex design choices explicit when the researchers omit technical language describing their study designs.
Problem
Research questions and friction points this paper is trying to address.
Difficulties in specifying experimental design plans clearly
Challenges in communicating and comparing design alternatives
Lack of formal language for constructing experimental assignment procedures
Innovation
Methods, ideas, or system contributions that make the work stand out.
Grammar of experimental design with composable operators
Domain-specific language PLanet for assignment plans
Three-stage construction: unit, trial-order, order-to-unit
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