Optimizely (YC W10) Increases Homepage Conversion Rate by 29%

It's an interesting combination with mixpanel. If we could get one more thing from Mixpanel, we'd really be set - I'm bugging them, we'll see if they come out with it!

A Bayesian would understand this in terms of prior probabilities and likelihood ratios, but to put it into nontechnical terms, suppose that you tried out 15 different alterations and none of them seem to work. Then on the 16th, your detector goes off and says, "Less than 5% probability of these results arising by chance!" Do you conclude that it's 95% likely that this version is genuinely better? No, because the first 15 failed attempts told you that improving this webpage is actually pretty hard (the prior probability of an effective improvement is low), and now when you see that the 16th attempt has a result with a less than 5% probability of arising from chance, you figure "Eh, it's worth testing further, but probably it is just chance."

Another extremely important point is that the classical statistics you learned to use to decide that something was some trial, that your running total of results is "statistically significant", when A and B are actually identically effective, is considerably greater than 5%. See http://lesswrong.com/lw/1gc/frequentist_statistics_are_frequ... - a trial I ran with 500 fair coinflips had at least one step where the cumulative data "rejected the null hypothesis with p You're not really to blame for this mistake, because the horrid non-Bayesian classical statistics taught in college are just about impossible to understand clearly; but it does sound to me like someone at your org needs to study (a) Bayes's Theorem (b) the case for reporting likelihood ratios rather than p-values (likelihood ratios are objective, p-values decidedly not) and (c) the beta distribution conjugate prior (which would make progress toward having priors and likelihood ratios over "These two pages have a single unknown conversion rate" or "These two pages have different unknown conversion rates"). Or in simpler terms, "Someone at your company needs to study Bayesian statistics, stat."

While Optimizely is strictly A/B testing, VWO is a much more comprehensive testing tool. In addition to A/B testing, we support Multivariate testing, Split URL testing, Heatmap/Clickmap, Targeting/Segmentation and Revenue Tracking. VWO is being used by companies that have sophisticated and comprehensive testing needs (like AMD, Microsoft, Groupon, etc. and thousands of SMBs)

That said Optimizely may be slightly easier to use but with VWO our focus is on both: comprehensiveness of feature set _AND_ ease of use. Since both companies offer free trials, best is to signup for both of them and see which one is better suited to your needs.

Lets say I roll a dice two times and get a six both times. The probability of this happening is 1/36 or about 3%.

Would you say I have established with a 95% confidence interval that the particular die I'm using always rolls six? No, because you have good reason to believe that the the results are in fact random. Or in other words, you have a strong prior belief that the hypothesis you're testing is false.

Yes, that is true for the dice roll; You already know most die aren't rigged.

I don't see how this affects the results of optimizely's product; You do not have a strong prior belief if the hypothesis is true or false.

Also note that the number of observations in the article used were in the thousands.