The Data Science of Experimental Design
Offered By: LinkedIn Learning
Course Description
Overview
Learn how to design an A/B test for a web page, run the test, analyze the data, and make decisions based on the results of the test.
Syllabus
Introduction
- Conducting an experiment online
- What you should know
- Experiment is a type of study
- Features of an experiment
- Circumstances for experimental testing
- When not to do an experiment
- Systems ready for experimental testing
- Comparability of experimental conditions
- Trying to increase conversions
- Different types of conversions
- Case definition of conversion
- Measuring a conversion
- Considering time period for conversions
- Rates versus frequencies of conversions
- Identify and prioritize conversions
- Operationalize counting conversions
- Document conversion case definitions
- Brainstorm denominators
- False positives and negatives
- Document denominators
- Determine time frames
- Baseline time-series analyses
- Data handling
- Baseline results as a guide
- Thinking about increasing conversions
- Strategies to increase conversions
- Planning a campaign
- Designing the test
- Testing the implementation
- Choosing a test statistic
- Choosing the chi-squared test
- Chi-squared test in Excel
- Installing G*Power
- Using G*Power
- Sample size simulation
- Planning the timeline
- Stratified analysis
- Conditional tests
- Overall analysis approach
- Time-series analysis
- Chi-squared analysis
- Interpretation
- What actions can we take?
- Report writing
Taught by
Monika Wahi
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