Seasonal Bias

A time series is seasonally biased if it contains more data from one part of the year than another. For example, an 18-month dataset covering two winters and one summer is seasonally biased in that it contains more winter data than summer data. The simple mean temperature from that dataset would probably be lower than the long-term average at that location because it incorporates more winter data than summer data.

One way to correct for seasonal bias is to calculate the mean of monthly means (MoMM) rather than the simple mean. The point of the MoMM is to give equal weight to each month of data even if dataset contains an unequal amount of data from different months.