Two kinds of information may be useful in making decisions: a description of seasonal fluctuation and a description of the variation in a series with the seasonal fluctuation removed. The two major approaches to defining seasonability are mathematically similar, but have practical differences. The component method emphasizes a separate description of seasonal fluctuation, while the stochastic approach emphasizes forecasting the future with a model that incorporates seasonality. Thus, the former focuses on seasonality itself, while the latter addresses seasonality as it affects the accuracy of a forecast. Component methods discussed include the moving average, additive/multiplicative assumption, the F of stable seasonality, the relative contribution of the irregular, the average duration of run, months for cyclical dominance, pattern consistency, and trading day option. Stochastic methods explained are moving average and autoregressive processes, identifying the process of a series, stationarity, and model evaluation. Appropriate applications of each method are described. The report notes that choice of a method depends on the study's objectives and the analyst's judgment. Tables and graphs are included. An annotated bibliography of 110 references is appended.