The Data Science of Experimental Design

The Data Science of Experimental Design
The Data Science of Experimental Design
English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 3h 35m | 436 MB

Interested in learning how to create an online experiment that helps you better understand your business? This course can help you get up to speed. Instructor Monika Wahi shows learners without a background in experimental design how to build an A/B test for a web page, run the test, analyze the data, and make decisions based on the results of the test. Monika begins by explaining exactly what A/B testing is and under what circumstances it is useful. She then covers potential strategies for increasing conversion rates, as well as how to choose both A and B conditions for testing. Next, she explains how to define conversion rates and develop and document case definitions, conduct a baseline analysis in Excel and, based on the results of the analysis, design an A/B test. Plus, she demonstrates how to conduct a chi-square test in Excel and get a sample size estimate using G*Power.

Table of Contents

1 Conducting an experiment online
2 What you should know
3 Experiment is a type of study
4 Features of an experiment
5 Circumstances for experimental testing
6 When not to do an experiment
7 Systems ready for experimental testing
8 Comparability of experimental conditions
9 Trying to increase conversions
10 Different types of conversions
11 Case definition of conversion
12 Measuring a conversion
13 Considering time period for conversions
14 Rates versus frequencies of conversions
15 Identify and prioritize conversions
16 Operationalize counting conversions
17 Document conversion case definitions
18 Brainstorm denominators
19 False positives and negatives
20 Document denominators
21 Determine time frames
22 Baseline time-series analyses
23 Data handling
24 Baseline results as a guide
25 Thinking about increasing conversions
26 Strategies to increase conversions
27 Planning a campaign
28 Designing the test
29 Testing the implementation
30 Choosing a test statistic
31 Choosing the chi-squared test
32 Chi-squared test in Excel
33 Installing G Power
34 Using G Power
35 Sample size simulation
36 Planning the timeline
37 Stratified analysis
38 Conditional tests
39 Overall analysis approach
40 Time-series analysis
41 Chi-squared analysis
42 Interpretation
43 What actions can we take
44 Report writing