How does bandit differ from the classic A/B?

Bandit (Multi-armed Bandit) differs from traditional A/B testing in that it dynamically allocates traffic between variants based on their current performance, rather than fixedly dividing users into groups.

Main differences:

• Adaptability: Bandit algorithms constantly update traffic distribution, favoring more successful variants, which allows for faster identification of the optimal option.
• Exploration and Exploitation: Bandit balances exploring new variants and exploiting already successful ones, reducing losses from showing less effective options.
• Decision Speed: Bandit can identify the best variant more quickly, especially with limited traffic.

In traditional A/B testing, traffic is fixedly distributed (e.g., 50/50) until the test ends, which can lead to losses if one variant is clearly worse.

Example: if you have two button variants, Bandit will gradually increase traffic to the more clickable variant, while traditional A/B testing will show both variants equally until the end of the experiment.