Statistics & AB Testing
Overview and objectives
In this course we'll go through the lifecycle of A/B testing and how to tackle experimentation questions in the interview. At the end we'll focus on some general statistical knowledge and questions that occur in data interviews.
The audience for this course is anyone that wants a comprehensive understanding of A/B testing and statistics across multiple domains and case studies.
Courses in this learning path are:
Introduction to Statistics and A/B Testing
In this lesson, we're going to go over problems you might face in interviews focused on A/B testing and statistics.
2 of 2 Completed
Hypothesis testing covers the fundamental theory and background behind A/B Testing. In this course we'll cover Z and T test, multiple hypothesis testing, and the different type errors.
12 of 12 Completed
A/B Testing & Experiment Design
Let's start with a general framework for A/B testing. In practice, an A/B testing and experimentation all follow a step by step process of setting metrics and designing experiments.
3 of 10 Completed
Confidence intervals help us deal with this imprecision by giving us a way to talk about a range of values with some certainty where the true value of the statistic is contained in.
2 of 6 Completed
A/B Testing Common Scenarios
The next couple of chapters will cover common scenarios and concepts involved in A/B testing. As A/B testing involves statistical concepts, there may be terms that you need refreshing on.
3 of 9 Completed
A/B Testing Tradeoffs
There are scenarios where A/B testing is not necessarily the best course of action. Often, there are technical, infrastructure, or practical concerns that come up while planning an A/B test.
2 of 6 Completed
This is a refresher on some important statistical concepts that will help us with A/B testing and beyond. While by no means a comprehensive guide, this chapter will go over some important basics about statistical testing and probability distributions.
1 of 11 Completed
Generalized Linear Models and Regression
Regression models are used to predict the value of a dependent variable from one or more independent variables.
8 of 13 Completed