But if we don't do it, we will end up missing important developments and could seriously misinterpret the aggregate data. Third, there are established techniques for looking through the noise in data. This is important for avoiding ‘excuse-making’ – stripping out the inconvenient movements while leaving in the ones that line up with the story you want to tell.
We do need to look through the lumps and bumps and noise to discern the underlying trend. But if we just excluded certain movements in an ad hoc way, we could be accused of bias, or we could miss something important.