You don’t need the code to think statistically. Scan for four things: Surprise (the p-value under the null), Scope (degrees of freedom… how many comparisons the test effectively carried), Size (effect size with an interval), and Setup (design, sampling, measurement). If any link is weak, temper the headline. A tiny p with a tiny effect in a giant sample is a rounding error to your decision. A big effect with a canyon-wide interval needs more data, not more adjectives. Ask: what would replication likely do… shrink, stabilize, or vanish? Good readers don’t worship outputs; they interrogate them.
