Static typing checks the types of expressions before a program is allowed to run. If a type mistake is caught at this stage, the program does not have to reach the faulty operation for the mistake to become visible. C/C++, Java, Fortran, and Rust are examples of statically typed languages. In dynamically typed languages such as Python and Julia, values have types and type checks happen while the program runs. section 1.1.1 section 1.9
Exploring a large dataset
Suppose you load a large dataset, inspect a plot, and choose your next calculation based on what you see. A static compile–launch–load workflow repeats that cycle after a code change, including the costly data load. An interactive workflow lets you change the code and inspect the result without reloading the dataset. This is a comparison of workflows, not a claim that typing and compilation are the same thing. section 1.3.1 section 1.1.1
The attraction of static typing here is early type-error detection and a path toward high-performance languages. The tradeoff motivating Julia is practical: scientific work may need both fast computation and rapid experimentation. Julia is dynamically typed yet designed for high performance through just-in-time compilation and multiple dispatch, aiming to avoid maintaining separate exploratory and performance-oriented implementations. section 1.9 section 1.1.1 section 1.4
