OBJECTIVES: Deep brain stimulation (DBS) is increasingly being used to treat a variety of neuropsychiatric conditions, many of which exhibit idiosyncratic symptom presentations and neural correlates across individuals. Thus, we have utilized inpatient stereoelectroencephalography (sEEG) to identify personalized therapeutic stimulation sites for chronic implantation of DBS. Informed by our experience, we have developed a statistics-driven framework for unbiased stimulation testing to identify therapeutic targets. MATERIALS AND METHODS: Fourteen participants (major depressive disorder = 6, chronic pain = 6, obsessive-compulsive disorder = 2) underwent inpatient testing using sEEG and symptom monitoring to identify personalized stimulation targets for subsequent DBS implantation. We present a structured approach to this sEEG testing, integrating a Stimulation Testing Decision Tree with power analysis and effect size considerations to inform adequately powered results to detect therapeutic stimulation sites with statistical rigor. RESULTS: Effect sizes (Cohen's d) of stimulation-induced symptom score changes ranged from -1.59 to +2.59. The standard deviation of sham trial responses was strongly correlated with the standard deviation of stimulation responses (r = 0.86, p < 0.001), and thus could be used to estimate the variability of stimulation responses for power analysis calculations. We show that 12-15 sham trials were needed to robustly estimate sham variability. Power analysis (using a paired-t test) showed that for effect sizes ≥ 1.1, approximately 10 trials should be used per stimulation site for sufficiently powered results. CONCLUSIONS: The workflow presented is adaptable to any indication and is specifically designed to overcome key challenges experienced during stimulation site testing. Through incorporating sham trials, effect size calculations, and tolerability testing, the described approach can be used to identify personalized, unbiased, and clinically efficacious stimulation sites.