The analysis of experimental data often involves statistical and computational methods. Machine learning and data mining techniques are increasingly being used to handle large datasets and to uncover patterns that might not be evident through traditional analysis. Techniques like Principal Component Analysis (PCA) and cluster analysis are employed to reduce the dimensionality of the data and to identify key variables.
