SUMMARY: Since the advent of agriculture, plant breeding has successfully improved plantsfor human benefit. Modern plant breeding activities consist in evaluating the genetic merit of lines discerning genetic from environment and noise components. To do so, modern plant breeding relies on the genetics foundations derived from Mendel?s work and statistical tools (or biometry) generated afterwards. Plant breeding activities could be grouped in three categories:traditional, marker assisted (MAS), and genomic selection (GS). Traditional plant breeding uses either per sephenotypic information, or information from relatives to evaluate the genetic value. MAS on the other hand, involves the identification of markers linked to genes or quantitative traits loci (QTL) of relevant traits, and then selecting individuals based on their marker scores. Finally, GS involves the prediction of the genetic merit of individuals based on their marker scores and a statistical model. All of the three strategies require the evaluation of large number of individuals creating massive amounts of data that needs proper analyses. Our objective was to present some biostatistical strategies that are successfully being used in plant breeding programs. First, we used novel simulation