The basic model that Optimizely uses is a Z-Test approximation of a binomial distribution. To run a proper experiment with that model, you should be calculating the sample size ahead of time, and then run it. Each visitor should be independent, and not affected by things like the day of the week, or the time of it. The end result tells you if the distributions are different, but not as much as one would think about the size of the differences. It also can't be 100. The normal distribution has an infinite range, so a finite limit can never capture 100% of it.

Optimizely is in a rough spot. People don't like having to think through experimental design, and they are really, really bad at reasoning about p-values. To try to fix the people part, they came out with the sequential stopping rule stuff (their "stats engine), but they never really published much justifying it. The other alternative would be to move the experiments into a Bayesian framework, but that has a lot of it's own problems. When they acquired Synference, that was one of the likely directions to take (along with offering bandits), but that didn't work out and those guys have since left.