A. Selecting the best model 1. We build models to try to answer biologically relevant questions. We use models to try to predict how a variable will act in a system or how the entire system will respond to an individual variable or a group of variables. A basic concept behind building any type of model selection criteria is finding a balance between bias and variance. Bias is the difference between the expected value and the true value.
Variance is the mean squared deviation of individual observations from their mean.
Minimization of the variance leads to a more accurate estimate. The fewer the number of terms in the model, the lower the variance due to fewer estimations that need to be calculated. Conversely, the more terms included in the model, the smaller the bias. AIC is a statistical calculation used to determine how many terms are appropriate for the model given the situational data. An AIC statistic is calculated for each model, constantly comparing it to the full model or the model containing all of the possible variables.