The Penicillin Myth

Scientific Takeaways from the Article

• Readers appreciated the clear explanation of how penicillin works (inhibiting bacterial cell-wall rebuilding) and the window into lab practice: routine work, lab notebooks, and meticulous observation.
• The piece is praised for showing science as process innovation plus curiosity, not a single lightning-bolt moment.

Competing Narratives of Fleming’s Discovery

• One camp stresses that the “core story” is intact: a contaminated plate, a clear zone of dead bacteria, Fleming’s curiosity, and his isolation and publication of penicillin. On this view, calling it a “myth” is overstated.
• Others emphasize Hare’s and Root‑Bernstein’s reconstructions:
  • The “open window” contamination tale is likely misremembered and back‑formed for public consumption.
  • A cold snap plus proximity to a mycology lab make an accidental but non-miraculous contamination plausible.
  • Root‑Bernstein’s version reframes the discovery as a byproduct of systematic, tedious work, not pure luck.

Serendipity vs Systematic Effort

• Several comments argue the important lesson is that breakthroughs often need both:
  • A large, methodical search space (lots of plates, lots of trials).
  • Openness to unexpected anomalies (a weird plate worth preserving and following up).
• Some see this as a better narrative for justifying sustained research funding than the “happy accident” trope.

Reproducibility and Scientific Writing

• One thread notes Fleming’s original 1929 paper is methodical, reproducible, and focused on the finding, not the story. The later anecdote is “mythmaking,” but the science itself replicated quickly.
• Others highlight that scientific papers compress vast background work into a terse, linear narrative, which naturally omits circuitous real‑world steps.

Mendel, Probability, and “Suspiciously Perfect” Data

• A long tangent uses Mendel’s near-perfect pea ratios to debate probability:
  • Exactly 100 heads in 200 fair coin flips is ~5–6% probable and is the single most likely count, but far less likely than “anything not exactly 100.”
  • Discussion drifts into Bayesian vs frequentist thinking, p‑hacking, and why “too perfect” data can raise suspicion even when each specific outcome is individually unlikely.

“Myth” vs “History” Framing

• Some object that branding the story a “myth” implies falsehood where the deviations are minor matters of sequence, temperature, or window status.
• Others argue “myth” in the sense of a polished, culturally convenient story is exactly what happened, and dissecting it clarifies how science and its narratives really work.