DISTRIBUTIONSCORE

Poisson Arrivals

Counts of rare independent events per window: calls, mutations, clicks.

λ mean ≈ variance
counts per window · recreated schematic

How it works

  • Events arrive independently at a steady average rate λ.
  • Variance equals the mean — one parameter does everything.
  • Windows scale cleanly: double the time, double λ.
Use → Staffing, queues, buffers. Don't use when events cluster or cascade.
Watch out → Bursts, schedules, or contagion violate independence — reach for negative-binomial instead.
Try it → Count support tickets per hour for a week; compare mean vs variance.
REDCAPE jobs → Predict · Design
Free sources → OpenIntro Statistics (CC BY-SA) • Wikipedia: Poisson distribution (CC BY-SA)
◍ Mentor soonSee REDCAPE playbook →

Attach to a lattice — combines with

Different families, shared jobs. Disagreement between them is where insight lives.

DIFFUSION
Bass Diffusion (S-Curve)
Innovators plus imitators make the S-curve. Spend early on seeds, later on social proof.
SHARED JOBS: PREDICT · DESIGN
LEARNING
Diversity Prediction Theorem
Crowd error equals average error minus diversity. Diverse models beat the lone genius.
SHARED JOBS: PREDICT · DESIGN
LINEAR & NONLINEAR
Categorical & Threshold Models
Buckets and cutoffs turn messy attributes into testable if-then rules.
SHARED JOB: PREDICT

More in Distributions

Normal Distribution
Bell-curve averaging: many small independent effects sum to a predictable center with thin tails.
Central Limit & Six Sigma
Sample averages settle to normal; variation reduction pays until returns diminish.
Power Law / Long Tail
Rich-get-richer: a few huge winners, many tiny players. Straight line on log-log.
← Power Law / Long TailEntropy & Uncertainty →