Final answer:
Unexplained variation is the part of variation in data that cannot be accounted for by statistical models, seen as the randomness remaining after considering the effects of explanatory variables. It is crucial in fields like genetics where natural and induced variability can arise from genetic differences such as SNPs and structural changes in the genome.
Step-by-step explanation:
The term unexplained variation refers to the portion of variation in a set of data that cannot be accounted for by a statistical model or the predictor variables used in the analysis. When we create a model, like a regression line, to explain or predict a response variable based on one or more explanatory variables, the unexplained variation is the sum of the squares representing variation within samples that can't be explained by the model itself - essentially, the noise or randomness that remains in the data after accounting for the effects of the explanatory variables. This is captured by the formula SSwithin = SStotal - SSbetween, where SS stands for the sum of squares.
In the context of genetic variation, when scientists look at outcomes like body fat percentage among different groups, they might attribute variations to measurement variability, natural variability, induced variability, or sampling variability. Additionally, the presence of genetic differences among individuals can lead to unexplained variation in data from biological or medical studies, such as varied responses to drug treatments due to individual genetic constitutions represented by single nucleotide polymorphisms (SNPs) and structural variation in genomes.