Sample Size

Sample size determination is crucial for ensuring the reliability and validity of scientific studies and machine learning model evaluations, aiming to balance statistical power with resource efficiency. Current research focuses on optimizing sample size calculations across diverse contexts, including A/B testing, neural network training, and causal inference, often employing techniques like Bayesian methods, Kalman filtering, and mixed-effects models to account for temporal correlations and data heterogeneity. These advancements improve the precision of estimates, enhance the generalizability of models, and ultimately lead to more robust and reliable conclusions in various scientific disciplines and practical applications.