One main purpose of statistics is to show developments, changes and trends over time in a comparable manner. When comparing data for two different points in time, for example two different months, these points in time might not necessarily be fully comparable due to certain occurrences, such as a different number of working days or the presence of atypical events. Thus, data need to be adjusted in order to show the real "unbiased" development or trend. Seasonal adjustment is a statistical technique by which the effect of seasonal occurrences are estimated and then removed from time series.
One example is that normally sales of businesses increase around Christmas time. So when comparing for example the months of December and July, the amount of sales for the month of December will in most sectors probably be higher than for July as it is influenced by the seasonal effect caused by Christmas. To compare retail sales without being influenced by the effect of Christmas sales, the data need to be seasonally adjusted.
Further information
- Video INE Spain Seasonal adjustment
- Video Croatian Bureau of Statistics Seasonal Adjustment