[highd]

Sorry you're getting down-voted, I don't think it's an unreasonable question.

In the sense I think you're using it, "statistics" are really methods for dimensionality reduction - we take means, and medians and standard deviations with the hopes that they will capture the parts of the data we care about. This is important for two reasons - for one, for anything even moderately high dimension we'll never have enough data to be able to forego some means of aggregation due to the "curse of dimensionality". Secondly, the human-machine interaction information bandwidth is annoyingly low, so we need some way to compress any information for human consumption. "Statistics" are one way we do so.

"Statistics" is also a field of study based around understanding how multiple data points relate to each other - that is of course critical to machine learning, and I think the terminology collision is why you're getting downvoted.