The section was established in 1973 by a team of researchers specialising in experimental theory and biometrics. Professor Tadeusz Przybysz led the section until 2000, after which Professor Mirosława Wesołowska-Janczarek took over. She held the position until 2011. Dr Izabela Kuna-Broniowska then took the lead and remained in charge until 2020. On 1 October 2020, the department was renamed the Laboratory of Experimental Theory and Biometry, with Dr Urszula Bronowicka-Mielniczuk becoming its new head.

Research 

Scientific research conducted at the Laboratory focuses primarily on the theory of experiment and applications of mathematical statistics in agricultural sciences, including agricultural engineering. Research topics include linear models of one and many variables, analysis of variance and covariance of various experimental systems, analysis of crop rotation experiments, estimation of parameters and study of their properties, study of effectiveness of systems, analysis of factorial experiments taking into account a control object, regression analysis, as well as analysis of growth curves taking into account accompanying variables. It also covered methods for estimating variance and covariance components in random and mixed models, and problems associated with determining sample size:

• development of single and multivariable linear models for systems in crop rotation experiments
• determine conditions for estimability of parameters and hypothesis testing
• determination of the efficiency of these systems
• methods of analysis of variance and covariance
• provide an evaluation of the relative power of formulations in the most commonly used experimental systems used in agricultural research
• to generalise Henderson's methods for estimating variance components to multiple variables
• to develop different models for growth curves with time-varying background variables
• Clarification of the conditions for estimability of parameters in the different variants of the profile sum model
• generalisation of the Behoffer's theorem for determining sample sizes
• to provide a new indicator for testing the goodness of fit of estimated functions
• demonstrating the applicability of the multivariate growth curve method for the determination of regression equations and their comparison for different groups of objects under the assumption of heteroscedasticity of variance
• comparing different tests for verifying hypotheses on the identity of regression equations for the studied groups