# Statistics & AB Testing

Jay

Published

9 Courses

## 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.

## Audience

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

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

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.

11 of 11 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

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

### Statistics

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.

4 of 11 Completed

### Data Analytics Fundamentals: Causal Inference

In this course we’ll go over the core concepts of causality, significance, and analyzing data. This is meant as a quick refresher and a high level overview of causal inference basics to eventually apply them in data analytics problems.

2 of 9 Completed

### Generalized Linear Models and Regression

Regression models are used to predict the value of a dependent variable from one or more independent variables.

9 of 13 Completed

**Good job, keep it up!**

## 49%

**Completed**

You have **39** sections remaining on this learning path.