I’m so overwhelmed with so much assignmnets currently. And the worst part is doing the subject that i’m not so in to it, statistic parametric, normality test in particularly!
Why data have to be normally distributed? That is my big question for the normality test inventor/creator. In real life no data set is ever exactly a Normal Distribution, so we won’t ever expect to see a straight line!. And the most rediculous thing is if data are unnormally distributed, we have to transform it by many extraordinary method so our data become normally distributed, ohhh…pleasee…
Normality testing is a waste of time why. With small samples, the normality test has low power. But when you have large samples, normality tests become ridiculously powerful, but they don’t tell you anything you didn’t already know. No real quantity is exactly normally distributed. The normal distribution is just a mathematical abstraction that’s a good enough approximation in a lot of cases. The simplest proof of this is that there is no real quantity (at least none that I can think of) that could take any real number as its value. For example, there are only so many molecules in the universe. There are only so many dollars in the money supply. The speed of light is finite. Computers can only store numbers of a finite size, so even if something did have a support of all real numbers, you wouldn’t be able to measure it.
The point is that you already knew your data wasn’t exactly normally distributed but the normality tests tell you nothing about how non-normal the data is. They give you absolutely no hint as to whether your data is approximately normally distributed such that statistical inference methods that assume normality would give correct answers. Ironically, common tests (e.g. the T-test and ANOVA) that assume normality are more robust to non-normality at large sample sizes.
hmmmm…what can i do now just: do my assignment, finish it, there is no need a deeper understanding because it is only an assignment, and “Ada ilmu yang bermanfaat dan harus dikuasai. Tapi, ada juga yang cukup sebagai pengetahuan semata”.