Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in...

Area of Science:

• Scientific methodology
• Biostatistics
• Data science

Background:

• Replicability is crucial for scientific discovery and clinical utility.
• A significant replicability crisis is impacting scientific research.
• Current statistics struggle to differentiate measurement error from individual differences.

Purpose of the Study:

• To address the limitations of existing replicability statistics.
• To introduce a novel statistic, discriminability, for quantifying sample similarity.
• To demonstrate how optimizing discriminability can enhance experimental design and inference.

Main Methods:

• Proposed a novel statistic, discriminability, to quantify within-individual sample similarity.
• Evaluated discriminability across diverse data types (univariate, Gaussian, Euclidean, and non-Euclidean).
• Applied discriminability optimization to simulated and real-world datasets, including brain imaging and genomics.

Main Results:

• Existing statistics like intra-class correlation and fingerprinting inadequately distinguish accidental vs. systematic deviations.
• Discriminability effectively quantifies the degree of similarity among an individual's samples.
• Optimizing discriminability demonstrably improved performance in subsequent inference tasks across all tested datasets.

Conclusions:

• Discriminability offers a robust method for assessing and improving the reliability of scientific measurements.
• Optimizing experimental design for discriminability is a promising strategy to combat the replicability crisis.
• Reducing accidental measurement error through discriminability enhancement is key to advancing scientific rigor.