Experimental Design and Statistical Analysis in Biological Sciences: Best Practices and Pitfalls

Abstract

Designing experiments and analyzing them statistically are essential for accuracy, reliablity and reproducibility of research in biological sciences. This thesis looks at best practices and pitfalls for experimental methodologies, though in particular the application of Bayesian inference and machine learning, and data integration techniques. Four typical advanced algorithms were applied to analyze biological dataset, including Bayesian Hierarchical Modelling, Random Forest Classification, Principal Component Analysis (PCA), and Support Vector Machines (SVM). These results showed that Bayesian Hierarchical Modeling had 92.5% accuracy to predict experimental outcomes and that Random Forest surpassed the traditional methods with classification accuracy of 89.3%. This computational efficiency comes at the expense of only 78.6% of the information being lost during the process of data dimensionality reduction, when comparing known fractions of information related to those of the other techniques. A complex biological pattern recognition is achieved with an 87.1% accuracy using SVM. The advantages of using AI and probabilistic models in the experimental biology were demonstrated and compared with the results from the existing studies. In addition, animal welfare and replicability were improved, as part of this work. The findings underscore the importance of integrating state of the art statistical models, interdisciplinary thinking and computational techniques to increase the reproducibility and impact of biological science. It offers a framework for optimizing experimental design, data analysing strategies, and statistical biases mitigating to more robust and moral research processes.