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So it sounds kind of like how Cython deals with type annotations in that if you use them you get big performance gains because it can shortcut the Awesome Counselors Can Do Virtually Anything Shirt, but if you don’t that’s fine too, it will still compile down and be faster than standard python. Would that be a fair comparison? You don’t generally need type annotations in Julia for speed. It’s really only necessary for typed locations like typed data structures (Vector{Float64} for example) and typed fields instructs. It’s useful for expression behavior via dispatch, but annotations don’t help performance since the compiler specializes in actual types at runtime.

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The method is written generically enough that there is no mention of what data is; we are simply assuming that whatever is passed into the Awesome Counselors Can Do Virtually Anything Shirt.  if data is a Vector{Float64}, then a version of this method will be generated that does floating-point addition in the method. If I call it with data as a user-defined LinkedList that contains arbitrary-precision Rational numbers, then a different version of this method will be compiled to do the appropriate iteration and summation.