How To Increase Site Performance Through A/B Split Testing | UX Booth
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How To Increase Site Performance Through A/B Split Testing

Google Website Optimizer seems a bit dirty - this looks like a cleaner way

Ben Tilly presentation on A/B Testing. From start to extracting conclusions.

Enough samples is at least 10 yes and no results in each test

(via <a href="http://simonwillison.net/2009/Sep/13/effective/">Simon Willison</a>)

By Ben Tilly.

Some great recources for A/B-Testing

Normally a formal statistical treatment would be too difficult, but I'm here to rescue you with a statistically sound yet incredibly simple formula that will tell you whether or not your A/B test results really are indicating a difference.

1. Risk-averse design 2. Lack of cohesion 3. Quitting too early 4. Customer hitchhiking 5. Metrics doesn’t replace strategy

Decent A/B article that discusses the local/global maxima issue I've often talked about

a/b testing has shortcomings- be aware of them when doing product design.

need to read but the title seems interesting

Good warnings for A/B testing

How Not To Run An A/B Test

Interesting argument (with statistical rationalisation) explaining why A/B tests should be run to a fixed number of observations. Advises against pushing a winner too quickly.

A good article which suggests not ending an A/B test too soon just because it has reached 95% confidence.

RT @Jake_Hird: Light reading for a Friday afternoon: The ultimate guide to A/B testing http://ow.ly/23gk2 #yam

Website A/B testing you'll actually use