To understand this concept better, we’ll establish a base example which we will reference in the blog at different points: Stage 0 is the number with which every other stage is compared. These stages are stage 0, stage 1 and so on. In part 2 of the blog, we will look at how we get to the point in the A/B tests where the statistical significance is measured and calculated.Īs we discussed in part 1 of this blog, BlueStacks compares the performance of two different versions based on the number of users that reached certain stages defined right from the download of the said version until the user installs an app on the version. The higher the possibility that the improvement is due to the modifications done in the code between the control and variation, the greater will be the statistical significance value and the fewer chances that the improvement is observed due to random changes. To brush up, it is the likelihood that the improvement of the different stages is not a random result. In part 1 of this blog, we shared some statistical significance insights.
0 kommentar(er)
