This procedure gives us a number called a t-value, which is a measure of how many standard deviations the difference between the two means is. We’re not going to get into the details here, but a rule of thumb is that if the t-value is greater than 2, then the difference between the two means is significant. If the t-value is less than 2, then the difference is not significant. We’re going to calculate the t-value for each pixel in the image to determine whether that pixel has changed significantly following the event in question. You don’t need to know the details of the t-test to understand the results (but hopefully you’ve got an intuition for what it’s doing). If you’re interested in learning more about statistical tests of this sort, I teach a course on Data Science at the University College London, and have made all of the lectures and courseware open-source. The T-test lecture is here.
+This procedure gives us a number called a t-value, which is a measure of how many standard deviations the difference between the two means is. We’re not going to get into the details here, but a rule of thumb is that if the t-value is greater than 2, then the difference between the two means is significant. If the t-value is less than 2, then the difference is not significant. We’re going to calculate the t-value for each pixel in the image to determine whether that pixel has changed significantly following the event in question. You don’t need to know the details of the t-test to understand the results (but hopefully you’ve got an intuition for what it’s doing). If you’re interested in learning more about statistical tests of this sort, I teach a course on Data Science at the University College London, and have made all of the lectures and courseware open-source. The T-test lecture is here.