If you’re running AB tests, you’re surely running into concepts like “95% confidence intervals” and the “Null hypothesis”. And you’re already using “p-values”, too, though you may not know it.
Unfortunately there’s a great chance you’re using them wrong, and it’s having an impact on your AB testing program?
Here is a business person’s overview to help you avoid common problems and misunderstandings about p-values.
In a previous post describing a simple approach to de-seasonalizing your data, I covered how marketers can examine, at a rough level, the impact of seasonality on metrics. Obviously, your data science team would be looking at this data in
I want to show you a technique to fix another common time-series problem: seasonality. The article will illustrate a fast and simple way to de-seasonalize your data.
Now that the 2012 U.S. Presidential elections are over, there’s a bit of a buzz around why some folks thought the election would be close and others who, using some solid statistical techniques, predicted a definitive (and it turns out,