Statistical Approach to use to Run A/B Test
Below is the comparison between two statistical approaches Frequentist approach and the Bayesian approach:
Parameters |
Frequentist Approach |
Bayesian Approach |
---|---|---|
Definition |
It is used to determine whether there is a statistically significant difference between two variations. |
It enables you to quickly optimize the experiments for conversions. |
Foundation Principle |
It is based on Probability as Long-term Frequency. |
It is based on Probability as a Degree of Belief. |
Data Source |
It uses data from the current experiment. |
It uses the prior knowledge from previous experiments. |
Objective |
It is used to conduct tests and draw conclusions. |
It uses existing data to draw conclusions. |
Complexity |
It is relatively simple and more traditional. |
It is more complex in comparison to the Frequentist approach. |
Sample Size |
It requires a fixed sample size that is decided in advance. |
It allows continuous updating of the sample as more data is collected. |
Flexibility |
This approach is less flexible. |
This approach is more flexible. |
A/B Testing Framework
A/B testing is a proven way to improve your online strategy by comparing two versions of a webpage or app and seeing which one performs better based on user behavior. This article focuses on discussing the A/B testing framework.
Table of Content
- What is A/B Testing?
- Why Should You Consider A/B Testing?
- What Can You A/B Test?
- Types of A/B Testing
- Statistical Approach to use to Run A/B Test
- Steps to Conduct an A/B Test
- A/B Testing Process
- What are Variant A and Variant B?
- What is the Conversion Rate?
- What do you mean by Statistical Significance?
- Mistakes to Avoid While A/B Testing
- Challenges in A/B Testing
- A/B Testing and SEO
- Conclusion
- FAQs
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