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An assessment that an observed effect is unlikely due to random chance under the null.
DESCRIPTION
Statistical significance indicates that observed data are inconsistent with the null hypothesis beyond a pre-specified threshold (alpha). It is commonly assessed via p-values or equivalent test statistics. Significance does not imply practical importance, causality, or correctness of the model. Multiple comparisons require adjusted procedures to control false positives. Reporting confidence intervals and effect sizes provides better context for decision-making.
RELATED TERMS
p-value Confidence Interval Hypothesis Testing Effect Size Statistical Power Multiple Testing False Discovery Rate Null Hypothesis Family-wise Error Rate
COMPETING TERMS
Practical Significance Bayesian Credible Interval
KNOWLEDGE AREAS
Mathematics Data Science Testing & Validation
TARGET AUDIENCE
Data Scientist Data Analyst BI Specialist AI Product Manager IT Auditor
EXTERNAL SOURCES
- Wikipedia - Statistical significance
- NIST - Overview of hypothesis tests and significance
- Encyclopaedia Britannica - Encyclopedia article on statistical significance
TOP QUESTIONS
What does Statistical Significance actually tell you?
It indicates data are unlikely under the null hypothesis given alpha. It does not measure effect magnitude or real-world importance.
Category: Misunderstanding
How is Statistical Significance determined?
Compute a test statistic and p-value, then compare to alpha. If p ≤ alpha, declare significance; otherwise, fail to reject the null.
Category: Measurement
What is the relationship between p-values and Statistical Significance?
P-values quantify the extremeness of data under the null; significance is a decision based on thresholding the p-value at alpha.
Category: Concept
Why report effect sizes alongside Statistical Significance?
Effect sizes show magnitude and practical relevance. Significant but tiny effects can be unimportant in practice.
Category: Best practices
How does multiple testing affect Statistical Significance?
Multiple comparisons inflate false positives; control with Bonferroni, Holm, or FDR procedures. Pre-register hypotheses where possible.
Category: Risks
Is lack of Statistical Significance proof of no effect?
No, it may reflect insufficient power or noisy data. Consider confidence intervals and power analysis before concluding no effect.
Category: Misunderstanding
What alpha level should be used for Statistical Significance?
Commonly 0.05, but choose based on risk tolerance, domain, and multiplicity. High-stakes contexts may require stricter thresholds.
Category: Opinion
How do Bayesian methods compare to Statistical Significance?
Bayesian approaches assess posterior probabilities and credible intervals. They offer different interpretations and decision frameworks.
Category: Alternatives
How to avoid p-hacking when seeking Statistical Significance?
Pre-register analysis, avoid data peeking, correct for multiple tests, and report all tested hypotheses. Transparency reduces bias.
Category: Best practices