Will Rogers Phenomenon — when the Okies moved out, both states got smarter
Dr. R. Düsing · Osnabrück University
If someone moves from group A to group B, both group averages can rise — without any value changing.
What is this about?
A joke by Will Rogers: when the "Okies" moved from Oklahoma to California, the average IQ rose in both states. That works if the movers are below the average of the old state but above the average of the new one: leaving raises one average, arriving raises the other. The overall average stays the same, because only the assignment changes. In medicine this is called stage migration: better diagnostics assign cases to other disease stages, so every stage looks better — without real progress. (The IQ joke is a pure statistics gag, not a real statement about anyone.)
How to proceed
1. Who can move? — set the number of willing-to-move Okies (outlined in purple in the plot). 2. Moving to California — "start wave" or control by hand from 0 to 100%. 3. Average IQ per state (①) and each person (②) — both average lines move to the right, the overall average stands still. 4. Concepts — why the overall mean is immune and what stage migration means.
What to watch for
It is not a real effect: not a single individual value changes, only a label (the place of residence) moves. Sum and count of all values stay the same — that is why the overall mean is immune. The condition: both means rise exactly when the mover is below the average of the old group but above that of the new group. Suspicious is: "all subgroups improve" but the overall value stagnates. Then it helps to compare the whole group or to apply the same classification consistently.
Things to try
Start the wave and notice that the overall average stays put while both state averages rise. Then change the number of willing-to-move Okies.
What's next?
In Simpson's Paradox, too, the conclusion depends on how cases are grouped: same data, different grouping, different result.
US comedian Will Rogers quipped: "When the Okies left Oklahoma and moved to California, they raised the average IQ of both states." 😄 Tongue-in-cheek — but statistically entirely correct, and that's exactly what this tool shows. (No real claim about anyone, purely a statistics gag.)
Does / does NOT
Does: shows how simply reclassifying cases between two groups can raise the mean of both groups at once — without a single individual value changing. Does NOT: it is not a real effect, not a learning effect, not measurement error. The overall average stays exactly the same; only a label moves.
The mechanism
A case with value w moves from group A to B. Both means rise exactly when
x̄_B < w < x̄_A
The migrant is below average in A (leaving raises x̄_A) and simultaneously above average in B (arriving raises x̄_B). Here: Oklahoma has the higher average, California the lower one; the migrants sit in between — below Oklahoma's average but above California's.
The overall average doesn't change because no value itself changes — only the assignment. The sum and count of all values stay the same.
The controls
Willing-to-move Okies: how many Oklahoma residents are willing to relocate at all (outlined in purple in the plot). ▸ Start wave: plays the move automatically — the Okies migrate to California one by one (lowest IQ first), both mean lines visibly shift right. …or by hand: the same move, controlled manually (0% = no one has moved yet, 100% = everyone).
Where it gets serious
The same effect is called stage migration in medicine: better diagnostics reclassify cases into different disease stages, making the statistics of every stage look better — without any real progress (Feinstein 1985). Rankings, grading tiers, or league tables are just as susceptible whenever cases get regrouped.
What to do?
Don't compare group-specific means across different classification rules. Instead, look at the overall group (immune to reclassification) or apply the same classification consistently. Get suspicious when "every subgroup improves" but the overall value stays flat.
This many Oklahoma residents are willing to relocate at all — outlined in purple in the plot.
Move to California
…or by hand0 %
Both let the Okies move to California one by one (lowest IQ first). 0% = no one has moved yet, 100% = everyone.
Legend
in Oklahoma 🤠
in California 🏄
willing-to-move Okie
mean now (thick line)
mean at start (pale)
overall mean
📋 Example — Okies Move to California
Will Rogers' quip: "When the Okies left Oklahoma and moved to California, they raised the average IQ of both states." 😄
The migrants are below Oklahoma's average but above California's average — they sit exactly in between.
Question first: what happens to both states' averages when they move — do they rise, fall, or a mix?
Slide the migration wave up (or click "▸ Start wave") and watch.
① Average IQ by State
Oklahoma 🤠
—
—
California 🏄
—
—
USA overall
—
unchanged by the move
Moved
0
of 12 willing to move
No one has moved yet — slide the migration wave up.
② Each Point = One Person · Position = IQ
top lane = Oklahoma · bottom lane = California · thick line = mean now · pale line = mean at start
If you move a case from group A to B whose value lies below A's average but above B's average, the mean of both groups rises — even though no individual value changes. The overall average stays constant.
Why does the overall average stay the same?
A move only changes a label (place of residence), not a measured value (IQ). The sum and count of all values stay identical → the overall mean is immune to reclassification. Only the split shifts.
The serious side: stage migration
In medicine, better diagnostics reclassify cases into different disease stages — every stage looks better afterward, without any real progress (Feinstein 1985). Rankings and grading tiers are just as susceptible.
How to spot it / what to do?
Suspicious: "every subgroup improves," but the overall value stagnates. Solution: compare the overall group, or apply the same classification consistently.
→ Simpson's Paradox→ Regression to the Mean