Statistical Significance Testing

Statistical significance testing is the practice of deciding whether an observed experiment result is likely to reflect a real effect rather than random variation.

Key points

• Statsig frames statistical significance as a reliability filter for data-backed decisions: teams use it to separate meaningful signals from random noise ^[src-035].
• The workflow starts with a null hypothesis that assumes no effect and an alternative hypothesis that represents the expected difference or relationship ^[src-035].
• Teams choose a significance level alpha before analysis. Lower alpha values reduce false positives but can make true effects harder to detect ^[src-035].
• Statistical significance depends on design quality, not only on the final calculation: sample size, data quality, test choice, independence assumptions, and bias control all shape whether the result is trustworthy ^[src-035].
• The article emphasizes that statistical significance is not the same as business significance. Teams still need to inspect effect size and real-world impact before acting ^[src-035].
• Reliable significance testing also requires protection against avoidable error sources such as multiple comparisons, p-hacking, confounding variables, and causal overclaiming ^[src-035].
• Anthropic applies the same measurement logic to AI evals: model score differences should be reported with standard errors and confidence intervals so apparent benchmark wins are not confused with noise ^[src-067].
• For model evals, the correct test often needs benchmark structure: clustered questions require clustered standard errors, and model-to-model comparisons on shared questions should use paired differences ^[src-067].

Related entities

• Statsig
• Anthropic

Related concepts

• P-Value Interpretation
• Multiple Testing Correction
• Experiment Statistical Power
• A/B Testing Mindset
• Sequential Testing
• Parallel A/B Testing
• Statistical Model Evaluations
• Clustered Standard Errors in Evals
• Paired-Difference Model Evals
• Question-Universe Eval Framing

Source references

• ^[src-035] Jack Virag — “How to accurately test statistical significance” (2025-04-12)
• ^[src-067] Anthropic – “A statistical approach to model evaluations” (2024-11-19)