diff --git a/scripts/sim_stroop.py b/scripts/sim_stroop.py
new file mode 100644
index 0000000..3adcdca
--- /dev/null
+++ b/scripts/sim_stroop.py
@@ -0,0 +1,69 @@
+# SPDX-License-Identifier: MIT
+"""
+Simulate trial-level Stroop data (plausible undergrad study).
+Creates:
+  data/synthetic/psych_stroop_trials.csv
+  data/synthetic/psych_stroop_subjects.csv
+"""
+import os, numpy as np, pandas as pd
+rng = np.random.default_rng(42)
+
+N_SUBJ = 60
+N_TRIALS_PER_COND = 100
+CONDITIONS = ["congruent", "incongruent"]
+
+def simulate():
+    os.makedirs("data/synthetic", exist_ok=True)
+    # subject-level covariates
+    subjects = pd.DataFrame({
+        "subject": np.arange(1, N_SUBJ+1),
+        "gender": rng.choice(["F","M"], size=N_SUBJ, p=[0.6,0.4]),
+        "handedness": rng.choice(["R","L"], size=N_SUBJ, p=[0.9,0.1]),
+        # baseline processing speed (lower = faster RT offset)
+        "baseline_speed": rng.normal(loc=0.0, scale=1.0, size=N_SUBJ)
+    })
+
+    rows = []
+    for _, s in subjects.iterrows():
+        # random subject intercept (ms)
+        subj_offset = rng.normal(0, 80)  # between-person variability
+        # accuracy tendency (fast-but-error-prone archetype)
+        err_bias = rng.normal(0, 0.05)
+        for cond in CONDITIONS:
+            for t in range(N_TRIALS_PER_COND):
+                # base RTs: congruent faster than incongruent
+                base = 550 if cond == "congruent" else 650
+                # trial noise
+                noise = rng.normal(0, 120)
+                # baseline_speed: faster people shave time, slower add time
+                rt = base + subj_offset - 25*s["baseline_speed"] + noise
+
+                # accuracy: a bit worse for incongruent
+                p_correct = 0.97 if cond == "congruent" else 0.90
+                p_correct += -0.03*max(0, s["baseline_speed"]) + err_bias
+                p_correct = np.clip(p_correct, 0.75, 0.99)
+                correct = rng.random() < p_correct
+
+                # sprinkle outliers (anticipations & lapses)
+                if rng.random() < 0.03:
+                    rt = rng.normal(150, 30)   # anticipatory
+                if rng.random() < 0.04:
+                    rt = rng.normal(2300, 200) # lapse
+
+                rt = max(80, rt)  # keep sane
+
+                rows.append({
+                    "subject": int(s["subject"]),
+                    "condition": cond,
+                    "trial": t+1,
+                    "rt_ms": float(rt),
+                    "correct": int(correct),
+                })
+
+    trials = pd.DataFrame(rows)
+    subjects.to_csv("data/synthetic/psych_stroop_subjects.csv", index=False)
+    trials.to_csv("data/synthetic/psych_stroop_trials.csv", index=False)
+    print("Wrote data/synthetic/psych_stroop_subjects.csv and psych_stroop_trials.csv")
+
+if __name__ == "__main__":
+    simulate()