Entropy-transformed teissier distribution: A modern statistical framework for engineering, pharmaceutical, and metrological applications

Statistical methods are crucial for making informed decisions under uncertainty. This study introduces a new probability model designed to improve data analysis in engineering, pharmaceutical sciences, and metrology. Traditional models often struggle with complex data patterns, so we propose a statistical framework based on the flexible Teissier distribution. The model's key properties are explored, and parameter estimation is carried out using reliable statistical techniques, supported by simulation studies. Real-world data from engineering, pharmaceutical, and metrological applications are used to assess the model's accuracy. Statistical tests confirm its consistency and precision. The entropy-transformed Teissier distribution shows strong adaptability, making it a practical and effective choice compared to existing methods.