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Funny cuz i wrote an article with similar reference to Poisson distribution
http://www.buildinganempire.com/poisson2.html
skype = "adultdatelink"Comment
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Well, actually, you seem to missunderstand the math.Funny cuz i wrote an article with similar reference to Poisson distribution
http://www.buildinganempire.com/poisson2.html
This is actually false. lambda varies with the number of visitors, k varies with the number of sign-ups, the probability depends on both.The interesting point of all this is that the error depends only on the number of signups and not the number of visitors
Obviously, things fall apart from there on,
Which is patent nonsense.Most people think, ok, I'll send a certain number of visitors to a sponsor and see how many signups I get. What you should be doing instead is saying ok, I'll send a certain number of signups to a sponsor to test them.
Affiliates have to make the following business decission : I've sent X visitors to a sponsor with 0 conversions so far, how long do I give it before I pull the plug ?
With the connoted question of, how likely is this to be a mere statistical artefact, and how likely is it to be a bad fit for conversions ?
So, it's nice that you've tried, but I think you should try re-reading on the subject.
PS. Do the exercises too, it will help.Last edited by fluffygrrl; 05-21-2008, 08:02 AM.Comment
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Priceless, your article, it turns out. Thanks for linking.
Sending enough hits to test a sponsor There's a concept in statistics called standard deviation. The idea is that 68% of the time a quantity will be within one standard deviation of the average. 95% of the time one expects to find the quantity within two standard deviations of the average. When a scientist measures some quantity they quote it with an error measurement which is one standard deviation. No measurement is certain. I will just refer to the standard deviation as the error, but you will know that technically I mean the standard deviation. I've also left a few technical details out such as how confident we are in our measurements because well, I'm only going to take this so far.I always thought standard deviation is somehow related to the statistical variance, for whatever reason. But I guess we might as well re-christen variance and call it the average. Altho I always thought it's a mean not an average. But I guess we could re-christen means to be averages too.In the Poisson Distribution the error is simply the square root of the average.
Bloody hell I haven't laughed this much in months.
68%.Comment
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As this page says - "Under the conditions where the Poisson distribution is applicable, the standard deviation may be approximated by the square root of the mean." Is it the word "approximately" you felt is missing? Or maybe you would prefer "mean" over "average" but I think most people would understand average better.Priceless, your article, it turns out. Thanks for linking.
I always thought standard deviation is somehow related to the statistical variance, for whatever reason. But I guess we might as well re-christen variance and call it the average. Altho I always thought it's a mean not an average. But I guess we could re-christen means to be averages too.
Bloody hell I haven't laughed this much in months.
68%.
skype = "adultdatelink"Comment
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Of course you have to make a business decision which is exactly why i stated that "No mathematical distribution can make a decision for you, but they can give you a very good idea of when enough is enough. The question then becomes, what probability level is enough for you?" with further elaboration both before and after.
skype = "adultdatelink"Comment
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A mean is not the same as an average Colin.As this page says - "Under the conditions where the Poisson distribution is applicable, the standard deviation may be approximated by the square root of the mean." Is it the word "approximately" you felt is missing? Or maybe you would prefer "mean" over "average" but I think most people would understand average better.
Other than that, you're confusing lambda with k. in my formula, or e and m in yours, or a and x in the formula on this latest page you quote.
Let's get them clear :
lambda, e and a equal the EXPECTED number of occurences. Thus, for 1:100 conversions, and 900 clicks, lamba, e and a equal 9.
k, m and x equal the ACTUAL number of occurences. Thus, that'd be the number of actual sign-ups.
Now, the standard deviation, which is the root of the the variance, can be approximated as the square root of the mean of lamba, e or a, depending on which formula you use.
Which makes it the square root of the number of expected signups, not actual signups, as contrary to
Much more interesting are the points you don't answer to, namelyIn the Poisson Distribution the error is simply the square root of the average. In other words, the error is the square root of the number of signups.
1. The false statement that signup probability is independent of the number of visitors,
2. The false statement that one should not think "I'll send X visitors" but instead "I'll send X sign-ups"
The entire point of using this math is to establish how many visitors to send, and under which feedback conditions.
And while you claim math can not make a decission for you, that is only because you don't quite know how to use it.
Properly applied, this bit of math will tell you, after having sent x visitors, if it's worth trying to send k more, or not.Last edited by fluffygrrl; 05-21-2008, 01:21 PM.Comment
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meds?
great article thanksFunny cuz i wrote an article with similar reference to Poisson distribution
http://www.buildinganempire.com/poisson2.html
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