# What statistical test should I use if I want prove for this hypothesis?

I have a point $x$ and a dataset $Y$ and I want to prove that $x$ cannot be getting from the dataset $Y$. More specifically, the point $x$ is a value I got from a classifier (it's not related to accuracy or probabilities, it interprets what the classifier has learned) and the data points in $Y$ were got by shuffling the labels and building the classifier model 1000 times. In other words, I'm trying to neglect the hypothesis that the point $x$ can be produced if you shuffled the labels, which points out that $x$ came out from the knowledge that the classifier has learned. I tried the t-test to prove that the $x$ is away from the mean of $Y$ but I'm not sure if this statistical test is sufficient.

• No statistical test can prove a hypothesis; it can only provide information that might cause you to change your belief as to whether the hypothesis is indeed true. Those who already believe firmly in the truth of the hypothesis get nothing from the new knowledge; as far as they are concerned, the hypothesis is true. But for those who are more skeptical initially, the results of the experiment might cause them to be less skeptical (or it might not). – Dilip Sarwate Jul 4 '18 at 19:24