= Probability plot
A <probability plot> compares <order statistics> with plotting positions or model <quantiles> to diagnose whether a proposed <probability distribution> describes data. If the model can be transformed into a linear identity $T(F(x))=a+bS(x)$, plot $T(i/(n+1))$ against $S(X_{(i)})$. Approximate linearity supports that model's shape; systematic curvature suggests a mismatch.
The plotting position $i/(n+1)$ comes from the exact mean of the $i$th <uniform order statistic>, by the <probability integral transform>. It does not assert equality between the expectation of a nonlinear transformation and that transformation of the expectation. Fitted slope and intercept can estimate model parameters, but the visual plot alone is not a calibrated <hypothesis test>.
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