Marcia Ferreira Hair Color, Body Shape, Family Members, Father, Mother, Brother, Sister, Marriage and Affairs

advertising. Now compare your two lists. Wherever you find an ad that is exactly the same months later, you have an ad that is very likely to be profitable. Put that URL on your ôprofitableö list. Put the other URLs that were on your first list, but are missing on your second list on your ôunprofitableö list. Cool; now you have a list of profitable and unprofitable sites. You can now run all kinds of quick statistical analysis on these sites. Since you can review a hundred sites much, much faster than you can usually get 100 results in a split testà you are way ahead now. Now, simply go through your ôproftiableö list and figure out the percentage of prices that have a ô7″ in them. Now go through your unprofitable list and figure out the percentage of prices that have a ô7″ in them. Compare the two numbers. Are they very close to each other? Then using a ô7″ probably doesnÆt matter very much in your pricing when it comes to profitability. Are they very distant? Did you find that 83% of unprofitable sites used a ô7″ in their price, but only 21% of profitable sites did? Woo hoo! You have found your answer in just an hour instead of waiting for days for a split test. So how do you know if you had a statistically significant number of sites to test? Yikes; with something this complex, that would take a couple of years of statistical training to figure that out. Let me give you a real world way of finding out that will save you from the hell of majoring in statistics in college (although if you are a single male, I highly recommend doing the majorà for some reason statistics chicks are much better looking than chicks taking other majorsà statistically speaking). Predictability. Turn around your study and do a split test. What percentage of the time did the results of the split test match the predicted outcome of the statistical analysis. The closer you get to 100%, the more you can rely on the dataset of profitable and unprofitable sites you built. Of course, 0% isnÆt the bottom of the scale hereà 50% is. So if you are only a bit over 50%, then you realy need to look at the assumptions of your studyà and the sample sizeà but if you are well over 75%, then you have a decent dataset that you can rely on for future studies. You might be thinkingà wait! I still had to do a split test! And worseà building that list of profitable and unprofitable sites took me just as long as it would take to do a split testà so what gives? You only have to build

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