What we see: a Data Sufficiency percent question. The box starts with 16 red, 6 green and 6 blue beads, 28 in all. 8 come out, and we need how many of those 8 were blue.
Step 1. Fix the one number every statement uses.
What we see: 28 beads minus 8 removed.
What it means: 20 beads are left. Each statement gives a percent "of the beads that remain," so each percent is a percent of 20.
What we do and why: turning a percent into a count makes each statement easy to test.
Step 2. Statement 1 alone.
What we see: 60% of the remaining beads are red.
What it means: \(0.6 \times 20 = 12\) red are left, so \(16 - 12 = 4\) red were removed. The other 4 removed beads are green or blue.
What we do and why: those 4 could be 4 green and 0 blue, or 0 green and 4 blue, or anything between. The blue count is not fixed. Not sufficient.
Step 3. Statement 2 alone.
What we see: 15% of the remaining beads are blue.
What it means: \(0.15 \times 20 = 3\) blue are left.
What we do and why: the box started with 6 blue, so \(6 - 3 = 3\) blue were removed. One value. Sufficient.
The trap: C. Statement 1 gives a full, neat result (4 red removed), so it feels like part of the answer. But it tells us nothing about how green and blue split, and Statement 2 already answers the question by itself.
Faster way: ask which statement talks about blue. Only Statement 2 does, and a blue count left plus the blue count at the start gives the blue count removed.
Takeaway: when the total after a change is known, a percent becomes a count. Start count minus end count gives how many were removed.
Answer: B
