Identifying outliers in a data set is conceptually cut and dry. Running the Grubbs test on the opposite extreme return - a near 49% loss - also finds no support for labeling the number an outlier.

Robust estimation and outlier detection play a critical role in modern data analysis, particularly when dealing with high-dimensional datasets. In such contexts, classical statistical methods often ...

Alfred Lin, who just celebrated his 14th year at Sequoia Capital, talks about the frameworks he uses to identify outlier startup founders.

The small-cap Russell 2000 is was slightly lower early Wednesday, taking a breather after posting an 11.5% return over the previous five trading sessions. DataTrek Research's Nicholas Colas, in a note ...