Bootstrapping is used to overcome budgets constraints  https://www.overleaf.com/read/kdvqgpkxnhjt 

Study group page updated http://www.research.ibm.com/haifa/dept/vst/ML-studygroup.shtml

In such ideal assumptions the central limit theorem can be used to create a confidence interval https://www.overleaf.com/read/kdvqgpkxnhjt (see section 2).

We'll discuss the concept of empirical distribution to better motivate the non parametric confidence interval we have just discussed https://www.overleaf.com/read/kdvqgpkxnhjt

A Python example of a non parametric confidence interval with unlimited sampling https://colab.research.google.com/drive/1mtweroXDGrUFs6OC1wDcKNNwP7vAR7Co#scrollTo=R3d2LVgf0Kgw&line=10&uniqifier=1

We'll cover how to create a non-parametric confidence interval.
https://drive.google.com/file/d/1P1JmqWT9xS5i5sPuXAVCwVIDxxr23qJu/view?usp=sharing

NP use for simple against complex tests.   https://drive.google.com/file/d/1IiAcgj_xarGrhIi_J_7HXFmyPxilrlZn/view?usp=sharing