Original structure: each yearly cohort completing program X reports a higher rate of downstream outcome Y than the prior cohort; therefore program X has been improving Y each year. The conclusion is a causal-attribution claim that the program drove the year-over-year rise. The flaw is that rising cohort outcomes do not establish the program as the driver, because environmental shifts (a strengthening local labor market) could equally produce the rising employment rate.
(B) mirrors both the structure and the flaw: each yearly cohort from a curling-coaching academy scored more than the prior cohort, and the argument attributes the rise to the academy's improvement, ignoring that league-wide scoring trends or weaker opponents could equally drive it. Same data shape (sequential year-over-year cohort increases), same institutional-attribution gap.
(A) is a cross-section comparison, not a longitudinal cohort claim. (C) compares single prize winners after a selection event, not cohorts after a uniform intervention. (D) and (E) are within-institution improvements the data genuinely supports (the automaker designs each car; the dairy controls each batch), so neither carries the original's flaw of attributing externally driven outcomes to the institution.
This is an advanced parallel-reasoning item: the task is to match argument structure and the shared causal-attribution gap, not to find a missing premise or a weakener.