The main theme of this session was: Why Design of Experiments (DoE) should be in every Data Scientist’s toolbox. Indrani started the session by introducing DoE and why it is needed by using an example of functional teams taking the challenge to increase activation rate…and explained the scenarios when the experiments are not set-up and when experiments are un-coordinated. Indrani continued the session by discussing the significance of Hypothesis Testing, P-value, Level of significance, Statistical Power, Sample size and Effect size. She further detailed the interpretation of results and the need for an A/B/n framework for DoE. Indrani concluded the session after discussing the Advanced concepts of DoE, especially on Adaptive DoE which helps consume the learnings from the previous set of experiments and design more focused, intelligent and efficient experiments the next time.

Thank you, Indrani for this insightful session.