Visually Explained: P-value and Hypothesis Testing

Who is this session for

• Data Analysts

• Data Scientists

• Product Managers

• CRO Strategists

• Data Science Students

• Any true data driven decision-maker

• Anyone who is struggling with statistics interviews

  
  

Who Am I

I am a Senior Data Scientist currently at Apple working in Experimentation Science team. I was a eBay in similar role for over 3 years where I helped transform experimentation culture with robust statistics and infrastructure. There I've led multiple training classes similar to this one to audiences over 200+ product managers, data scientists, data analysts, engineering managers, software engineers to help them understand statistics and conduct better experiments.

What this class is about

tl;dr; - You will learn statistics in a way you will never unsee or forget (and confidently ace the statistics round in interviews). 

This was a LIVE whiteboarding training session attended by over 610 data professionals and students. Please see feedback from the event at the end of the description.

A/B Testing statistics can be very confusing to people not trained in statistics OR not using it as part of their daily job. However, some statistical aspects are a must requirement to get a job as a Data Analyst, Data Scientist, and Product Manager in online/SaaS companies for making informed decisions. Often times there's even a power struggle between people who know statistics and people who make decisions. To build great products, these two segments of people should highly overlap but that's not the case. 

With this class, I intend to spread the knowledge of A/B testing explained like never before. If you're a decision-maker then you'll highly benefit from this session. If you're a data analyst or data scientist this class will help you translate statistical jargon to your business audience. 

The class will be mostly on a whiteboard as if you're being taught at a school rather than a boring presentation. I will take you under the hood of an a/b test and simplify statistical concepts.

Few Questions that you'll be able to answer with ease after the session

1. What is A/B testing? (Experimentation)

2. What is Frequentist A/B Test Approach and its assumptions?

3. What is hypothesis testing?

4. What is Null Hypothesis and Alternate Hypothesis?

5. How do you explain p-value to anyone? 

6. What is significance level (aka. alpha threshold)?

7. What is Power of the test?

8. What is Power Calculation OR Sample Size Calculation?

9. How do you explain Type I error and Type II error?

10. What are some of the ways of minimizing False-positive rates?

11. What is the Central Limit Theorem? Why is it important?

Feedback from Event

DISCLAIMER: The content of this event is for educational purposes only and does not include any specific facts of the presenter’s current or previous company. The opinions expressed in this event are solely those of the presenter based on personal experiences and do not reflect those of the company the presenter works for.