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LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d0_10   [metric: # successes (count)]
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model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = # successes (count))
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
observed groups: stim n=3, control n=8   nrep=120, alpha=0.05

--- fitted on real data ---
stim x log(day) interaction: F(1,111)=3.705  p(resid)=0.05681  p(Satt)=0.05696 (df=105)
  honest per-animal random slope (log-day): F(1,8.2)=2.03  p=0.1913
Cohen's f (interaction, partial eta^2=0.007) = 0.086  (small; f: .10 small, .25 medium, .40 large)

--- power simulation (log-day ground truth: stim:logday=+8.428, ratSD=8.126, resSD=14.95) ---

 true stim:log(day) = +8.428 (100% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.17            |  0.35  <- observed
  5        |  0.44            |  0.56
  8        |  0.68            |  0.69  <- observed
  12       |  0.91            |  0.92
  16       |  0.95            |  0.96
  24       |  0.99            |  1.00

 true stim:log(day) = +4.214 (50% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.07            |  0.07  <- observed
  5        |  0.08            |  0.14
  8        |  0.23            |  0.29  <- observed
  12       |  0.28            |  0.28
  16       |  0.55            |  0.53
  24       |  0.63            |  0.62

Read per-animal as the honest power; LME matches the paper's power code (optimistic).