The seasonality of population data has been of great interest in demographic studies. When
seasonality is analyzed, the population at risk plays a central role. In a
study of the monthly number of births and deaths, the population at risk is the product of the
size of the population and the length of the month. Usually, the population can be assumed to
be constant, and consequently, the population at risk is proportional to the length of the
month. Hence, the number of cases per day has to be analyzed. If one studies the seasonal
variation in twin or multiple maternities, the population at risk is the total number of
monthly confinements, and the study should be based on the rates of the multiple maternities.
Consequently, if one considers monthly twinning rates, the monthly number of birth data is
eliminated and one obtains an unaffected seasonality measure of the twin maternities. The
strength of the seasonality is measured by a chi-squared test or by the standard deviation.
When seasonal models are applied, one must pay special attention to how well the model fits the
data. If the goodness of fit is poor, it can erroneously result in a statement that the
seasonality is slight, although the observed seasonal fluctuations are marked.