Two Plots, One Season
why comparing a fertilized field to last year's unfertilized field proves almost nothing, and what that reveals about the whole point of a controlled experiment.
Part 1
Part 2
What the Agronomist Is Actually Testing
the real difference between noticing a pattern in your data and rigorously proving that one specific change caused it.
Part 3
Judging by a Good-Looking Season
why relying on gut feel and a single good result is exactly the trap randomized experimentation was invented to escape.
Part 4
Randomizing the Plots
why randomly assigning which plot gets the treatment is the single most important design decision in any real experiment.
Part 5
The Control Plot
why every real experiment needs a group that gets no change at all, and why 'no change' is a genuinely informative baseline, not a wasted opportunity.
Part 6
One Variable at a Time
why changing the fertilizer, the seed, and the watering schedule all at once makes it impossible to know which change actually mattered.
Part 7
How Big Does the Field Need to Be
why testing a new fertilizer on three plants tells you almost nothing, and how statistical power determines the minimum trial size that's actually worth running.
Part 8
The Season Wasn't Long Enough
why checking a trial's results too early, or ending it too soon, produces misleading conclusions even with a perfectly designed experiment.
Part 9
A Rogue Weed in the Data
how a bug in the assignment logic, a broken tracking pixel, or a mislabeled group can silently invalidate an otherwise well-designed experiment.
Part 10
Reading the Yield
what a p-value actually means, and the specific, narrow claim it does and doesn't support.
Part 11
A Difference That Doesn't Matter
why a statistically significant yield increase of half a gram per plant might not be worth the cost of the new fertilizer at all.
Part 12
Testing More Than One Fertilizer
how multivariate testing lets an experimenter test several changes at once without sacrificing the ability to isolate each one's real effect.
Part 13
The Neighboring Field Effect
why a treatment applied to one plot can quietly affect its untreated neighbor, silently violating one of experimentation's core assumptions.
Part 14
Different Soil, Different Result
why a fertilizer's average effect across the whole farm can hide the fact that it helps sandy soil and actually hurts clay soil.
Part 15
Harvesting Early
how sequential testing methods let an experimenter check results early and stop legitimately, without inflating the risk of a false conclusion.
Part 16
When You Can't Split the Field
what to do when randomization genuinely isn't possible, and why a quasi-experiment is a real, useful fallback rather than a lesser substitute.
Part 17
The Automated Greenhouse
how modern experimentation platforms automate randomization, power calculations, and analysis, turning rigorous testing into routine infrastructure.
Part 18
Too Many Trials at Once
why running a hundred simultaneous trials guarantees several will look significant by pure chance alone, and what to do about it.
Part 19
From the Test Plot to the Whole Farm
why a winning result on a small test plot doesn't automatically guarantee the same result once it's rolled out to the entire farm.
Part 20
The Complete Growing Season
reassembling the whole discipline, from two mismatched plots to a fully validated result rolled out safely across the entire farm.