Exercise 2: 200ms Average and 8-Second p99 — Possible Solution
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WHY BOTH CAN BE ACCURATE AT THE SAME TIME
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Per this chapter, "average" is "a single blended figure - easily
dominated by the bulk of fast, ordinary requests, hiding a smaller
group of genuinely slow ones," while p99 shows "what the slowest 1% of
requests actually experienced." If the large majority of requests are
genuinely fast (say, well under 200ms) and a small minority (roughly
1%) are extremely slow (around 8 seconds), the average across all of
them can still land near 200ms, since the fast majority mathematically
dominates the blended figure - while the p99, looking specifically at
the tail, accurately reports that the slowest 1% experienced something
much worse.

WHY THE AVERAGE ALONE MISSES THIS
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The average treats every request as equally weighted toward one single
number, which is exactly why it "hides" the slow minority - their
extreme values get diluted across the much larger number of fast
requests, the same mathematical effect Exercise 1 described for
averaging over time, here applied across requests instead.

WHY p99 MATTERS FOR DIAGNOSING COMPLAINTS
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Per this chapter, the slowest 5% or 1% of requests are "exactly the
group most likely to generate complaints." Users experiencing the
8-second p99 tail are the ones actually filing tickets, even while the
200ms average would suggest everything is fine - meaning the average
is actively misleading about where the real, complained-about pain is
coming from.

WHY THIS WORKS AS AN ANSWER
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It explains the specific mathematical reason both figures can be
simultaneously accurate (a fast majority diluting a slow minority in
the average), and connects the p99 figure directly to why it's more
useful for diagnosing user complaints than the average is.