This book introduces the principles and foundations of advanced statistical methods for designing experiments and testing hypotheses in life sciences. Advanced statistical methods, such as testing of hypotheses, recent methods of sample size determination/imputation, estimation techniques, probability distributions, and univariate analysis demonstrated with real data, and their integration into life sciences are included in the book. Advanced topics are presented with sufficient conceptual depth and examples to explain the use of recent statistical techniques and to demonstrate what conclusions can be drawn at the right time using modeling in life science research.

Key features:

• Explains the derivation of statistical models to prove disease transmission using massive real-world data sets to explore practical applicability

• Incorporates application of innovative advanced statistical and epidemiological models and demonstrates possible solutions for public health policy intervention

• Helps to understand the process of hypothesis testing in small or larger observations by using weighted parameters

• Presents suitable examples and real-life research datasets, and all models can easily be followed in formulating the statistical and mathematical derivations and key points

• Includes machine learning (AI), statistical methods for meta-analysis, testing of hypotheses, methods of imputation, estimation techniques, probability distributions, univariate analysis, and recent nonparametric methods, all illustrated through actual data.

This textbook is for students and scholars of various courses in life sciences, medicine, mathematics, and statistical science. It will also help academicians and researchers to understand the foundation of this topic.