In linear pharmacokinetics, systemic concentration over time data fits a one-compartment model. Typically, the concentration data may be matched to a single or multi-compartment PK model to help interpret pharmacokinetic equations and make therapeutic decisions. Most pharmacokinetics of drugs are performed without specifying the applicable mathematical model (non-compartmental analysis). One primary characteristic of linear models is that the change in concentration over time is constant. Thus, the compound’s half-life is constant and area under curve (AUC) pharmacokinetics increases linearly with dosage regardless of the body’s drug level. Therefore, doubling the dose will result in doubling the AUC, or administering half the dose twice per day will result in the same AUC as administering the total dose once per day. Contrastingly, non-linear pharmacokinetic models result from saturable behavior in the absorption, distribution, metabolism, or excretion of the compound. Here, saturable metabolism or excretion results in higher than dose-proportional increases in AUC pharmacokinetics with dosage, and saturable absorption results in lower than dose-proportional increases in AUC pharmacokinetics with dosage. In either case, the validity of PK parameters depends on the data’s quality entered into the model. Thus, dosing and sample collection must be precisely timed, and analytical methods must be sensitive and specific. Pharmacokinetics of drugs must be weighted appropriately to avoid bias due to the increased probability of errors at drug concentrations near the assay’s detection limit. Results obtained using a specific pharmacokinetic model should be compared to those using noncompartmental methods.