Roman's Data Science: How to monetize your data

At this stage, don’t take any of your statistics too literally and don’t let any single number dominate your strategic thinking. Just as we’re not looking for statistical significance at this point, we also don’t want to start treating our results as if they are indisputable facts.

As a pragmatic matter, it’s more common to test the hypothesis of 0 difference than some other hypothetical difference.

When companies perform A/B tests, experimenters report how one version changed a particular metric compared to the other version. They also report a statistic called a p-value, which shows the probability that the difference they observed was due to chance.[99] Usually, if p < 0.05 (i.e. there’s a less than 5% chance that the difference was just

For online offers, controlled A/B tests let you assess click-through and conversion rates on different behavioral pricing tactics. They give you statistically significant data on the options with the best outcomes. But you must set up these tests correctly, which includes clearly defining your control and test cases. You must also divide the sample

A/B and multivariate tools identify when treatments have reached a level of significance where we can make decisions. For example, a simple computation might show that version A has x participants of which y percent converted and version B has m participants of which n percent converted. You