Honeyweb Blog / Statistical Significance
Our blog has been set up to provide you with helpful information regarding maximising the effectiveness of your online marketing strategy.
There are many interesting independent articles and whitepapers here to download and links to some of the best resources on the net.
Why testing platforms give different sample size estimates
A/B testing is a powerful way to increase conversion (e.g., 638% more leads, 78% more conversion on a product page, etc.).
Its strength lies in its predictive ability. When you implement the alternate version suggested by the test, your conversion funnel actually performs the way the test indicated that it would.
To help determine that, you want to ensure you’re running valid tests. And before you decide to implement related changes, you want to ensure your test is conclusive and not just a result of random chance.
By Daniel Burstein
marketingexperiments.com
Honeyweb are your local website marketing experts, contact us to discover how we can help your business.
There is Nothing Magical About 95% Statistical Significance
You are probably ending your A/B tests either too early or too late.
The standard best practice in the conversion optimization industry is to wait until two conditions have been met before ending an A/B test.
First, that a representative sample is obtained. Second, that the winner of the test can be declared with 95% certainty or greater. You can see the latter standard touted here and here and here.
By Peep Laja
conversionxl.com
Honeyweb are your local Conversion marketing experts, contact us to discover how we can help your business.
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