P(bug | large) = 24/80 = 0.3.
The mechanical move is the whole lesson: conditioning is zooming. “Given large” means you discard the small-PR row entirely — those 120 PRs no longer exist for this question — and re-ask inside the 80 that remain. 24 of those 80 had bugs: 0.3. The denominator changed from 200 (the whole table) to 80 (the condition’s slice). That denominator change is conditioning.
Formally, for any events A and B:
P(A | B) = P(A and B) / P(B)
which in table terms is just: (cell count) / (slice total) — here (24/200) / (80/200) = 24/80. The 200s cancel; conditionals never care about the grand total, only about the slice.
And the number you were told to notice: P(large | bug) = 24/36 ≈ 0.67. Same cell on top — 24 large-and-buggy PRs — but a different slice on the bottom. “Given that it was large, was it buggy?” (0.3) and “given that it was buggy, was it large?” (0.67) are different questions with different answers, and the only difference is which total you divide by.
The pitfall this sets up: confusing those two is probably the single most consequential reasoning error in applied probability — it convicts innocent people (“probability of this evidence if innocent” read as “probability of innocence”) and misreads medical tests. We’ll hit it head-on in a few lessons; for now, the vaccine is the habit of always asking “which slice am I standing in?”
Where this goes: rearrange the definition above and you get P(A and B) = P(A | B) · P(B) — the multiplication rule, next lesson, and one more rearrangement after that is literally Bayes' theorem. Everything in this track is this table, viewed from different angles.