Monte Carlo Random Number Distribution Validation Toolkit

This toolkit is designed to validate whether Monte Carlo random number distributions satisfy statistical randomness criteria. The implementation includes three core validation modules: independence testing (using autocorrelation or runs tests), uniformity assessment (via chi-square or Kolmogorov-Smirnov tests), and parametric verification (distribution parameter estimation). These validation mechanisms help users ensure generated random sequences maintain true stochastic properties without predictable patterns or systematic biases. The program outputs quantitative metrics and visual analysis plots, enabling users to perform comparative studies and determine whether adjustments to random number generation algorithms (such as seed selection or algorithm parameters) are necessary. Given the widespread application of Monte Carlo methods in numerical computing and scientific simulations, this toolkit provides valuable quality assurance for random number generation, supporting more reliable implementation of stochastic algorithms in computational projects.