The article introduces a new way to measure randomness in data using pattern vectors, which can improve the accuracy of randomness testing compared to traditional methods like Runs Test. By analyzing patterns in binary sequences, the researchers developed a statistic to determine the randomness of data more effectively. They conducted experiments to show that their approach outperformed Runs Test in detecting randomness. Additionally, they created a reliable measure of randomness for any binary sequence and demonstrated how this new method can be combined with Kalman filters to improve data assimilation.