Summary: The risk of an individual woman having a pregnancy associated with Down’s syndrome is estimated given her age, \(\alpha\)-fetoprotein, human chorionic gonadotropin, and pregnancy-specific \(\beta\)1-glycoprotein levels. The classical estimation method is based on discriminant analysis under the assumption of lognormality of the marker values, but logistic regression is also applied for data classification. In the present work, we compare the performance of the two methods using a dataset containing the data of almost 89,000 unaffected and 333 affected pregnancies. Assuming lognormality of the marker values, we also calculate the theoretical detection and false positive rates for both the methods.