There is a peculiar irony embedded in how researchers and data practitioners have long approached statistical software. Tools that were designed to ease the burden of quantitative analysis have, over time, accumulated so many legacy conventions that operating them has become an expertise unto itself — separate from, and sometimes at odds with, the actual business of understanding data.
The question worth asking in 2026 is not "which software do I know?" but rather: "which environment most honestly serves my data and the conclusions I need to draw from it?"
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