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Without Conversion Rates You Don’t Know If You’re Mickey Mouse Or Mickey Mantle

 
Konstantin

Great reply...

>You can compare the standard error of the mean for each
>group as check how much overlap you get for these groups.
>You could have something like: with 95% confidence level,
>mean conversion rate for group A is 2.0-3.0%, for group B
>1.6-1.8%. With such numbers, you could be sure that group
>A indeed performed better. But you could've had group B
>with 0.7%-2.7% in which case, the distributions of means
>overlap too much to consider that data conculsive. In any
>case, you would not be able to calculate that without
>taking sample sizes (for each group individually) into
>account.

You are of course right. This is what we do with clients and for our own purposes. We measure conversion rates on mean averages per sampled 1000 visitors. However my article was 2000 words plus and I wanted to explain what conversion was and illustrate some of the things which effect it. You're so far ahead of the average reader of my articles that it's untrue. To give you an idea, one of the responses I had asked how he should go about measuring conversion with webalizer and told me his most visited page was style.css.
What I am saying is I was aiming at a less educated reader ;o). That said your observations are spot on.

>To make things even more interesting, there is one more
>way to look at your data. This method gives you the best
>guess as to the reliability of the data for small sample
>sizes, but is rarely used.

This is excellent. A method I've never used and something which might work for some of the B2B websites we work with which have smaller visitor counts. I'm pleased you posted this here because I learned something today.
 
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