The New Statistics¶
Cumming, G. (2014). The New Statistics: Why and How. Psychological Science, 25(1), 7-29.
Cited by¶
6 citations across 6 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Confidence Intervals
- The CI framework reveals interconnections with other primes: duality with #434 (hypothesis testing — a 95% CI excludes a value iff a two-sided test rejects at α=0.05); complementarity with #435 (statistical significance / p-value — CI communicates magnitude and precision while p-value communicates binary significance); synergy with #447 (effect size — CI-for-effect-size provides calibrated uncertainty around magnitude estimates); planning value with #437 (statistical power — expected CI width under alternative hypotheses informs sample-size calculations); dependence on #433 (sampling representativeness — valid CIs require design-based variance estimation for complex sampling); alternative to #444 (Bayesian updating — Bayesian credible intervals as conceptually distinct but numerically similar alternative)
This sourceThe defining manifesto of the 'New Statistics' (estimation, effect sizes, confidence intervals, and meta-analysis in place of dichotomous NHST/p-values).
- The CI framework reveals interconnections with other primes: duality with #434 (hypothesis testing — a 95% CI excludes a value iff a two-sided test rejects at α=0.05); complementarity with #435 (statistical significance / p-value — CI communicates magnitude and precision while p-value communicates binary significance); synergy with #447 (effect size — CI-for-effect-size provides calibrated uncertainty around magnitude estimates); planning value with #437 (statistical power — expected CI width under alternative hypotheses informs sample-size calculations); dependence on #433 (sampling representativeness — valid CIs require design-based variance estimation for complex sampling); alternative to #444 (Bayesian updating — Bayesian credible intervals as conceptually distinct but numerically similar alternative)
- Effect Size
- Hypothesis Testing (Null vs. Alternative)
- A statistically significant result can be trivially small in magnitude; a non-significant result can reflect either small effect or insufficient power
This sourceCumming new statistics effect-size confidence intervals point estimate plus uncertainty reporting discipline.
- A statistically significant result can be trivially small in magnitude; a non-significant result can reflect either small effect or insufficient power
- Statistical Power
- The CI includes zero and is centered near zero; the 5-BPA clinical-threshold for meaningful yield penalty is outside the CI upper bound (+1.7 < 5.0)
This sourceCumming new statistics effect-size confidence intervals point estimate plus uncertainty reporting discipline.
- The CI includes zero and is centered near zero; the 5-BPA clinical-threshold for meaningful yield penalty is outside the CI upper bound (+1.7 < 5.0)
- Statistical Significance (p-Value)
- Contemporary practice often employs the dichotomous threshold as if it were epistemically fundamental, losing continuous information
This sourceCumming new statistics effect-size confidence intervals point estimate plus uncertainty reporting discipline.
- Contemporary practice often employs the dichotomous threshold as if it were epistemically fundamental, losing continuous information
- Type I & Type II Errors
This sourceCumming new statistics effect-size confidence intervals point estimate plus uncertainty reporting discipline.
Verification¶
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