08-12, 13:45–14:25 (Asia/Yerevan), 113W PAB
Being part of statistical learning apparatus, and having a strong mathematical background AB testing remains one of the aspects in the field that continue to be violated and misinterpreted. A big part of violations covers the wrong experiment setup, which I'll try to cover in practice taking into consideration the business setup: whether it's a B2B platform or B2C. It would be nice if the audience had a hands-on experience with A/B testing, if not - I'm still going to cover it on a high level. The main takeaway for the audience will be to understand the pitfalls that relay under experiment setup, where a single disregarded use case can violate the whole experiment outcome. Time breakdown: 10mins A/B testing basics, 15mins Pitfalls, 5min Q&A
A/B testing fundamentals: How to do hypothesis testing, theory A/B testing in production: How to select the audience? Is it good to run several experiments simultaneously? When to interpret the results? Do we need to revisit the already completed experiment? What are A/A experiments?
Previous knowledge expected
I'm Sona, Data Scientist at ServiceTitan. I started my career as a big data analyst and then shifted toward data science. I love combining work and education thus I started teaching at the Armenian State University of Economics "Data analytics with Python" and "Into to machine learning" modules.