stat

To run rigorous statistical analysis on an OSINT investigation dataset, you must pre-register hypotheses, run a full test battery using two independent methods, and compute effect sizes. This process prevents p-hacking and ensures valid anomaly detection.

Preventing p-hacking during network forensics anomaly detection requires pre-registering your hypotheses before analyzing the data. You must run a full test battery of parametric and non-parametric methods, reporting all tests including non-significant results.

Yes, you can use Bayesian analysis to complement frequentist p-values in investigation data. The statistical analysis process requires computing effect sizes like Cohen's d alongside p-values and providing a Bayesian complement for all major findings.

Statistical analysis works directly with Prometheus metrics and timeline events to identify anomalies, correlations, and significance. The process applies rigorous statistical methods across various investigation datasets including node TX values and Prometheus metrics.

To distinguish correlation from causation in forensic statistics, you must apply rigorous statistical methods that compute effect sizes and provide Bayesian complements. The analysis outputs a report-ready markdown summary explicitly distinguishing correlations from causal relationships.

Relying solely on p-values for investigation data analysis risks p-hacking and ignores effect sizes. A complete statistical analysis requires pre-registering hypotheses, running a full test battery of two independent methods, and computing Bayesian complements to validate findings.