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LOG-DAY MODEL + COHEN'S f + POWER -- prev_f_full   [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=12, control n=12   nrep=120, alpha=0.05

--- fitted on real data ---
stim x log(day) interaction: F(1,227)=8.356  p(resid)=0.004218  p(Satt)=0.004253 (df=208)
  honest per-animal random slope (log-day): F(1,24.3)=3.32  p=0.08079
Cohen's f (interaction, partial eta^2=0.012) = 0.112  (small-medium; f: .10 small, .25 medium, .40 large)

--- power simulation (log-day ground truth: stim:logday=+2.389, ratSD=4.328, resSD=4.338) ---

 true stim:log(day) = +2.389 (100% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.15            |  0.28
  5        |  0.33            |  0.46
  8        |  0.61            |  0.65
  12       |  0.82            |  0.83  <- observed
  16       |  0.92            |  0.94
  24       |  0.99            |  0.98

 true stim:log(day) = +1.195 (50% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.05            |  0.09
  5        |  0.12            |  0.17
  8        |  0.20            |  0.26
  12       |  0.33            |  0.32  <- observed
  16       |  0.46            |  0.49
  24       |  0.46            |  0.49

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