Registry v0.4 · a new statistic content-tag over the corpus · every tag grounded in a verbatim, statistical-sense sentence · the paper list is at the end.
phenomenon facet, worded too specifically, shared a string
across only 1 pair of disciplines. Canonical statistical terms are the opposite: because a p-value,
a standard deviation or a hypothesis test means the same thing in physics, biology, psychology and
sociology, each bridges many fields at once — mean spans 41 disciplines,
statistical-significance 26, standard-deviation 37,
correlation 32, p-value 34. This is the field-invariant join key the
phenomenon facet was missing.
| Statistic | group | #disc | #papers | disciplines |
|---|---|---|---|---|
| mean μ | central | 41 | 112 | anthropologyart-historyartificial-intelligenceastronomybehavioral-economicsbehaviour-analysisbiologychemistrycognitive-psychologydigital-humanitiesearth-scienceecologyeconomicseducationenvironmental-scienceepidemiologygeneticsinformation-theorylawlinguistic-typologyliteraturemachine-learningmarketingmaterials-sciencemathematicsmedicinenetwork-scienceneurosciencephysicsphysics-education-researchpolitical-sciencepolitical-sociologypsychologyquantitative-financequantum-computingroboticsscience-educationsocial-psychologysociologystatisticsvisual-neuroscience |
| standard-deviation SD/σ | dispersion | 37 | 119 | art-historyartificial-intelligenceastronomybehavioral-economicsbiologychemistrycognitive-psychologycomputer-sciencedigital-humanitiesearth-scienceeconomicseducationexperimental-jurisprudencegeneticsinformation-systemsinformation-theorylinguistic-typologylinguisticsmachine-learningmarketingmedicineneurosciencephysicsphysics-education-researchpolitical-sociologypsychologyquantitative-financequantum-computingrehabilitation-neuroscienceroboticsscience-educationsleep-neurosciencesocial-neurosciencesocial-psychologysocial-sciencessociologystatistics |
| p-value p | inference | 34 | 103 | art-historyartificial-intelligenceastronomybehavioral-economicsbiologycognitive-psychologydigital-humanitiesearth-scienceecologyeconomicsevolutionary-virologyexperimental-economicsgeneticsinformation-systemslinguisticsmarketingmetasciencenetwork-scienceneurosciencephysicspolitical-economypolitical-sciencepolitical-sociologypopulation-geneticspsychologyquantitative-financerehabilitation-neurosciencescience-educationsocial-neurosciencesocial-psychologysociologystatisticsstrategic-communicationvisual-neuroscience |
| correlation r | association | 32 | 129 | anthropologyartificial-intelligenceastronomybiologychemistrycognitive-psychologyconsumer-psychologydigital-humanitiesearth-scienceecologyeconomicseducationexperimental-jurisprudencegeneticsinvertebrate-neurophysiologylawlinguisticsmarketingnetwork-scienceneurosciencephilosophypolitical-sciencepsychologyquantitative-financequantitative-psychologyquantum-computingroboticssleep-neurosciencesocial-neurosciencesocial-psychologysociologystatistics |
| regression β | association | 29 | 77 | artificial-intelligencebiologychemistrycognitive-psychologycomputer-sciencecontrol-theorydigital-humanitiesearth-scienceeconomicseducationexperimental-economicsgeneticshealth-psychologyinformation-theorylinguistic-typologylinguisticsmarketingmedicinenetwork-scienceneurosciencephilosophypolitical-sociologypopulation-geneticspsychologyquantitative-financeroboticssocial-psychologystatisticsvisual-neuroscience |
| statistical-significance | inference | 26 | 67 | artificial-intelligenceastronomybehavioral-economicsbiologycognitive-psychologycomputer-sciencedigital-humanitiesearth-scienceeconomicseducationepidemiologygeneticslawlinguisticsmarketingmathematicsmedicinenetwork-scienceneurosciencepolitical-sciencepsychologyquantitative-financescience-educationsocial-psychologysociologystatistics |
| t-test t | inference | 23 | 144 | art-historyartificial-intelligencebehavioral-economicsbiochemistry,-genetics-and-molecular-biologycognitive-psychologycomputer-scienceconsumer-psychologycultural-psychologyeconomicsinformation-systemslinguisticsmarketingmedicineneurosciencepolitical-sciencepsychologypsychophysicsquantitative-financerehabilitation-neurosciencescience-educationsensory-sciencesocial-neurosciencesocial-psychology |
| confidence-interval CI | dispersion | 23 | 68 | artificial-intelligenceastronomycognitive-psychologydecision-sciencesdigital-humanitiesearth-scienceeconomicseducationevolutionary-virologyexperimental-economicsexperimental-jurisprudencelinguisticsmedicineneurosciencepolitical-sciencepolitical-sociologypsychologyquantitative-financesleep-neurosciencesocial-neurosciencesocial-psychologysocial-sciencesstatistics |
| variance σ² | dispersion | 22 | 50 | |
| sample-size n/N | inference | 21 | 36 | |
| ANOVA F | inference | 20 | 121 | auditory-psychophysicsbehavioral-economicscognitive-psychologyconsumer-psychologycultural-psychologyeconomicsinvertebrate-neurophysiologymarketingmedicineneurosciencepolitical-sciencepsychologypublic-healthrehabilitation-neurosciencescience-educationsensory-sciencesocial-neurosciencesocial-psychologysocial-sciencesstatistics |
| Bayesian | advanced | 20 | 44 | |
| standard-error SE | dispersion | 16 | 36 | |
| hypothesis-testing | inference | 16 | 31 | |
| effect-size d | association | 15 | 31 | |
| bootstrap | advanced | 14 | 49 | |
| null-hypothesis H0 | inference | 13 | 18 | |
| chi-square χ² | inference | 13 | 16 | |
| median | central | 12 | 27 | |
| false-discovery-rate FDR | advanced | 10 | 19 | |
| Cronbach's alpha | 8 | 14 | ||
| odds-ratio OR | association | 8 | 8 | |
| chi-squared test | 7 | 22 | ||
| logistic regression | 7 | 19 | ||
| chi-squared | 7 | 17 | ||
| statistical-power | inference | 7 | 9 | |
| standard error of the mean | 6 | 64 | ||
| F-test | 6 | 15 | ||
| degrees-of-freedom df | inference | 6 | 8 | |
| Wilcoxon signed-rank test | 5 | 41 | ||
| coefficient of determination | 5 | 18 | ||
| Mann-Whitney U test | 4 | 30 | ||
| repeated-measures ANOVA | 4 | 25 | ||
| linear regression | 4 | 21 | ||
| Kolmogorov-Smirnov test | 4 | 19 | ||
| binomial test | 4 | 15 | ||
| Bayes factor | 4 | 11 | ||
| sensitivity | 4 | 5 | ||
| effect size (Cohen's d) | 4 | 5 | ||
| power-law | 4 | 4 | ||
| Wilcoxon rank-sum test | 3 | 25 | ||
| paired t-test | 3 | 20 | ||
| false discovery rate correction | 3 | 9 | ||
| Pearson correlation | 3 | 8 | ||
| multiple regression | 3 | 7 | ||
| p < 0.001 | 3 | 7 | ||
| meta-analysis | 3 | 6 | ||
| mean squared error | 3 | 5 | ||
| interaction effect | 3 | 5 | ||
| median split | 3 | 4 | ||
| 95% CI | 3 | 4 | ||
| Spearman rank correlation | 3 | 4 | ||
| confirmatory factor analysis | 3 | 4 | ||
| significance level (alpha) | 3 | 4 | ||
| Wilcoxon | 3 | 4 | ||
| poisson-distribution | 3 | 3 | ||
| proportion | 3 | 3 | ||
| precision | 3 | 3 | ||
| Cohen's d | 3 | 3 | ||
| Akaike information criterion | 3 | 3 | ||
| ordinary least squares regression | 3 | 3 | ||
| root mean square error | 3 | 3 | ||
| principal component analysis | 2 | 33 | ||
| cross-validation | 2 | 29 | ||
| Kruskal-Wallis test | 2 | 17 | ||
| general linear model | 2 | 13 | ||
| Fisher's exact test | 2 | 12 | ||
| maximum likelihood estimation | 2 | 11 | ||
| Bonferroni correction for multiple comparisons | 2 | 9 | ||
| Bonferroni correction | 2 | 8 | ||
| principal components analysis | 2 | 8 | ||
| significance level | 2 | 8 | ||
| likelihood ratio test | 2 | 7 | ||
| one-sample t-test | 2 | 7 | ||
| mean and standard deviation | 2 | 7 | ||
| coefficient of variation | 2 | 6 | ||
| post hoc multiple comparison test | 2 | 6 | ||
| Bayesian information criterion | 2 | 6 | ||
| partial correlation | 2 | 6 | ||
| power analysis | 2 | 6 | ||
| SD | 2 | 6 | ||
| post hoc test | 2 | 5 | ||
| effect size (partial eta squared) | 2 | 5 | ||
| repeated measures ANOVA | 2 | 5 | ||
| two-way ANOVA | 2 | 5 | ||
| z-score | 2 | 5 | ||
| Kruskal–Wallis test | 2 | 5 | ||
| one-way ANOVA | 2 | 5 | ||
| generalized linear model | 2 | 5 | ||
| specificity | 2 | 4 | ||
| Greenhouse-Geisser correction | 2 | 4 | ||
| arcsine transformation | 2 | 4 | ||
| Shapiro-Wilk normality test | 2 | 4 | ||
| paired-samples t-test | 2 | 4 | ||
| false discovery rate correction for multiple comparisons | 2 | 4 | ||
| z-test | 2 | 4 | ||
| explained variance | 2 | 4 | ||
| posterior probability | 2 | 3 | ||
| ANCOVA | 2 | 3 | ||
| z-score standardization | 2 | 3 | ||
| root mean squared error | 2 | 3 | ||
| statistical power analysis | 2 | 3 | ||
| area under the curve | 2 | 3 | ||
| credible interval | 2 | 3 | ||
| average variance extracted | 2 | 3 | ||
| maximum likelihood | 2 | 3 | ||
| OR | 2 | 3 | ||
| exponential-distribution | 2 | 2 | ||
| R-squared R² | association | 2 | 2 | |
| weighted average | 2 | 2 | ||
| Mann-Whitney | 2 | 2 | ||
| recall | 2 | 2 | ||
| partial eta squared | 2 | 2 | ||
| significance level (alpha), two-tailed testing | 2 | 2 | ||
| structural equation modeling | 2 | 2 | ||
| correlate | 2 | 2 | ||
| variance inflation factor | 2 | 2 | ||
| significant interaction | 2 | 2 | ||
| accuracy | 2 | 2 | ||
| generalized linear mixed model | 2 | 2 | ||
| signal-detection sensitivity (d-prime) | 2 | 2 | ||
| geometric mean | 2 | 2 | ||
| multiple comparison correction | 2 | 2 | ||
| Lilliefors normality test | 2 | 2 | ||
| deviance | 2 | 2 | ||
| activation likelihood estimation | 2 | 2 | ||
| latent profile analysis | 2 | 2 | ||
| MANOVA | 2 | 2 | ||
| descriptive statistics | 2 | 2 | ||
| statistical significance test | 2 | 2 | ||
| exploratory factor analysis | 2 | 2 | ||
| chi-square test | 2 | 2 | ||
| frequency count | 2 | 2 | ||
| standardization (z-scores) | 2 | 2 | ||
| mediation analysis | 2 | 2 | ||
| Likert scale | 2 | 2 | ||
| sensitivity and specificity | 2 | 2 | ||
| significance test | 2 | 2 | ||
| histogram | 2 | 2 | ||
| least squares | 2 | 2 | ||
| frequency distribution | 2 | 2 | ||
| log-normal distribution | 2 | 2 | ||
| AUC | 2 | 2 | ||
| RMSE | 2 | 2 | ||
| MAE | 2 | 2 | ||
| mean absolute error | 2 | 2 | ||
| percentage agreement | 2 | 2 | ||
| permutation test | 1 | 34 | ||
| multiple-comparison correction | 1 | 13 | ||
| mutual information | 1 | 10 | ||
| rank correlation | 1 | 9 | ||
| sign test | 1 | 8 | ||
| cross-correlation | 1 | 7 | ||
| ROC analysis | 1 | 7 | ||
| Spearman correlation | 1 | 7 | ||
| correlation coefficient | 1 | 7 | ||
| SEM | 1 | 6 | ||
| Friedman test | 1 | 6 | ||
| autocorrelation | 1 | 6 | ||
| Rayleigh test | 1 | 6 | ||
| Wilcoxon rank sum test | 1 | 5 | ||
| k-means clustering | 1 | 5 | ||
| Gaussian mixture model | 1 | 5 | ||
| Monte Carlo simulation | 1 | 4 | ||
| χ 2 | 1 | 4 | ||
| coherence | 1 | 4 | ||
| circular correlation | 1 | 4 | ||
| paired t test | 1 | 4 | ||
| Poisson test | 1 | 4 | ||
| independent component analysis | 1 | 3 | ||
| normality test | 1 | 3 | ||
| support vector machine classification | 1 | 3 | ||
| Wilcoxon signed rank test | 1 | 3 | ||
| z-score standardisation | 1 | 3 | ||
| d-prime (signal detection sensitivity) | 1 | 3 | ||
| median and interquartile range | 1 | 3 | ||
| Greenhouse-Geisser sphericity correction | 1 | 3 | ||
| singular value decomposition | 1 | 3 | ||
| exact binomial test | 1 | 3 | ||
| Kruskal–Wallis | 1 | 3 | ||
| p < 0.05 | 1 | 3 | ||
| Bayesian estimation | 1 | 2 | ||
| linear discriminant analysis | 1 | 2 | ||
| F | 1 | 2 | ||
| support vector machine | 1 | 2 | ||
| ridge regression | 1 | 2 | ||
| reduced-rank regression | 1 | 2 | ||
| signal detection sensitivity index | 1 | 2 | ||
| Mann–Whitney U test | 1 | 2 | ||
| paired t -test | 1 | 2 | ||
| Fisher information | 1 | 2 | ||
| Fano factor | 1 | 2 | ||
| Mann-Whitney U-test | 1 | 2 | ||
| Kullback-Leibler divergence | 1 | 2 | ||
| nonlinear curve fitting | 1 | 2 | ||
| psychometric function fitting | 1 | 2 | ||
| multitaper spectral estimation | 1 | 2 | ||
| binomial | 1 | 2 | ||
| rank sum test | 1 | 2 | ||
| signal detection sensitivity (d-prime) | 1 | 2 | ||
| correlation (coefficient of determination) | 1 | 2 | ||
| multiple-comparison correction (false discovery rate) | 1 | 2 | ||
| mean ± SD | 1 | 2 | ||
| Cohen's kappa (inter-rater reliability) | 1 | 2 | ||
| linear mixed-effects model | 1 | 2 | ||
| bootstrap confidence interval | 1 | 2 | ||
| kurtosis | 1 | 2 | ||
| modulation index | 1 | 2 | ||
| Akaike Information Criterion | 1 | 2 | ||
| ROC analysis / area under the curve | 1 | 2 | ||
| curve fitting | 1 | 2 | ||
| Bayesian Information Criterion | 1 | 2 | ||
| Kolmogorov-Smirnov normality test | 1 | 2 | ||
| Gaussian mixture model (classification) | 1 | 2 | ||
| Spearman rank correlation with permutation test | 1 | 2 | ||
| significance threshold (alpha) | 1 | 2 | ||
| mean square error | 1 | 2 | ||
| circular-linear correlation | 1 | 2 | ||
| cross-validated | 1 | 2 | ||
| Mann-Whitney test | 1 | 2 | ||
| cluster analysis | 1 | 2 | ||
| planned comparisons | 1 | 2 | ||
| post-hoc tests | 1 | 2 | ||
| analysis of covariance | 1 | 2 | ||
| Poisson distribution | 1 | 2 | ||
| Pearson | 1 | 2 | ||
| Consensus | 1 | 2 | ||
| kappa | 1 | 2 | ||
| Linear regression | 1 | 2 | ||
| p < .001 | 1 | 2 | ||
| probability-density | 1 | 1 | ||
| inter-rater-reliability | 1 | 1 | ||
| random-sampling | 1 | 1 | ||
| positive-predictive-value | 1 | 1 | ||
| prior-probability | 1 | 1 | ||
| probability | 1 | 1 | ||
| exponential-growth | 1 | 1 | ||
| extrapolation | 1 | 1 | ||
| order-of-magnitude | 1 | 1 | ||
| random-matrix-theory | 1 | 1 | ||
| negative-binomial | 1 | 1 | ||
| empirical-bayes | 1 | 1 | ||
| Krippendorff's alpha | 1 | 1 | ||
| inter-annotator agreement | 1 | 1 | ||
| F-score | 1 | 1 | ||
| mixed-effects model | 1 | 1 | ||
| inter-coder reliability on a 170-sentence sample: average correlation 0.91 and average Cohen's Kappa 0.82, called substantial agreement | 1 | 1 | ||
| the rise in parliamentary PDD: mean share 6.6% to 9.8%, reported with t = -105.24 and p < 0.01 alongside |d| = 0.13 | 1 | 1 | ||
| the gender difference: 0.066 against 0.055, called modest in the same sentence as significant, t = 2.613, p = 0.010, |d| = 0.268 | 1 | 1 | ||
| the corpus: 10,410 annotated sentences, 1,812 PDD-positive at 17.4%, and 642 carrying the second-stage annotations | 1 | 1 | ||
| linear estimator (linear decoding) | 1 | 1 | ||
| sparse coding (unsupervised dictionary learning) | 1 | 1 | ||
| principal-component-analysis | 1 | 1 | ||
| transfer entropy | 1 | 1 | ||
| phi (Φ) | 1 | 1 | ||
| EEG coherence | 1 | 1 | ||
| bits | 1 | 1 | ||
| current density | 1 | 1 | ||
| bispectral index | 1 | 1 | ||
| signal-detection sensitivity index | 1 | 1 | ||
| correction for multiple comparisons | 1 | 1 | ||
| Student t tests | 1 | 1 | ||
| cluster P < 0.05 corrected | 1 | 1 | ||
| interaction effect (factorial analysis) | 1 | 1 | ||
| Latin square counterbalanced design | 1 | 1 | ||
| d-prime (signal detection discriminability index) | 1 | 1 | ||
| hit rate and false-alarm rate | 1 | 1 | ||
| psychometric function | 1 | 1 | ||
| inverse of the cumulative normal function | 1 | 1 | ||
| linear fit | 1 | 1 | ||
| psychometric curves | 1 | 1 | ||
| mixed-effects regression | 1 | 1 | ||
| ANOVA / F-test | 1 | 1 | ||
| cluster-based permutation statistics | 1 | 1 | ||
| shuffle (permutation) test | 1 | 1 | ||
| shuffle test | 1 | 1 | ||
| mutual information rate | 1 | 1 | ||
| Post hoc power analysis | 1 | 1 | ||
| Monte Carlo simulations | 1 | 1 | ||
| Kruskal-Wallis | 1 | 1 | ||
| Cliff's delta | 1 | 1 | ||
| two-way repeated measures ANOVA | 1 | 1 | ||
| ANOVA across SNR conditions | 1 | 1 | ||
| STRF power normalisation | 1 | 1 | ||
| an alternative noise-aware shape-change method | 1 | 1 | ||
| sphericity correction | 1 | 1 | ||
| repeated measures ANOVA with Huynh-Feldt correction | 1 | 1 | ||
| linear regression of slopes across positions | 1 | 1 | ||
| conditional versus unconditional probability | 1 | 1 | ||
| cluster-level significance | 1 | 1 | ||
| F-test / p-value | 1 | 1 | ||
| mean and standard error | 1 | 1 | ||
| interaction term in linear regression | 1 | 1 | ||
| Cronbach's alpha (intercoder/scale reliability) | 1 | 1 | ||
| Greenhouse-Geisser correction for sphericity | 1 | 1 | ||
| correlation (test-retest reliability) | 1 | 1 | ||
| item response theory parameter estimation | 1 | 1 | ||
| repeated-measurement ANOVA | 1 | 1 | ||
| Greenhouse–Geisser correction | 1 | 1 | ||
| correlation analysis | 1 | 1 | ||
| standardization (z scores) | 1 | 1 | ||
| Type I error | 1 | 1 | ||
| nonparametric tests | 1 | 1 | ||
| action potential frequency | 1 | 1 | ||
| linear mixed model | 1 | 1 | ||
| learning-slope correlation | 1 | 1 | ||
| binomial-test | 1 | 1 | ||
| maximum likelihood estimation (robust) | 1 | 1 | ||
| root mean square error of approximation | 1 | 1 | ||
| comparative fit index | 1 | 1 | ||
| standardized root mean square residual | 1 | 1 | ||
| greatest lower bound reliability | 1 | 1 | ||
| full information maximum likelihood (missing data) | 1 | 1 | ||
| factor analysis (exploratory and confirmatory) | 1 | 1 | ||
| multinomial logit model | 1 | 1 | ||
| likelihood-ratio chi-squared (deviance analysis) | 1 | 1 | ||
| generalised linear model / generalised linear mixed model | 1 | 1 | ||
| ordinal regression (multinomial ordinal model) | 1 | 1 | ||
| inter-rater reliability (Krippendorff's alpha) | 1 | 1 | ||
| ANOVA (two-way repeated measures) | 1 | 1 | ||
| post hoc multiple comparison test (Holm-Sidak) | 1 | 1 | ||
| interaction effect in factorial ANOVA | 1 | 1 | ||
| two-way repeated-measures ANOVA with Holm-Sidak post hoc | 1 | 1 | ||
| light by virus interaction ANOVA | 1 | 1 | ||
| genome-wide RNA sequencing | 1 | 1 | ||
| Ingenuity Gene Network Analyses | 1 | 1 | ||
| quantitative RT-PCR | 1 | 1 | ||
| Pearson r | 1 | 1 | ||
| principal component analysis (cross-validated) | 1 | 1 | ||
| permutation/shuffling test | 1 | 1 | ||
| Fisher z-transformation | 1 | 1 | ||
| Wilcoxon right-tailed signed rank test | 1 | 1 | ||
| cross-validated Principal Component Analysis | 1 | 1 | ||
| shuffling method | 1 | 1 | ||
| power analysis / effect size | 1 | 1 | ||
| permutation/randomization test | 1 | 1 | ||
| statistical power / effect size | 1 | 1 | ||
| immediate-early gene expression | 1 | 1 | ||
| ordinal regression | 1 | 1 | ||
| χ 2 test | 1 | 1 | ||
| one-sample t-tests | 1 | 1 | ||
| partial report | 1 | 1 | ||
| Signal Detection Theory | 1 | 1 | ||
| false alarms | 1 | 1 | ||
| Drift Diffusion Model | 1 | 1 | ||
| lapse rate | 1 | 1 | ||
| random effects analysis | 1 | 1 | ||
| clustering algorithms for spike extraction | 1 | 1 | ||
| dimensionality reduction and point process techniques | 1 | 1 | ||
| correlations of individual movements | 1 | 1 | ||
| bouton density | 1 | 1 | ||
| puncta density | 1 | 1 | ||
| Rayleigh statistics | 1 | 1 | ||
| ANOVA with Dunnett's post hoc test | 1 | 1 | ||
| ANOVA with Newman-Keuls post hoc test | 1 | 1 | ||
| Dunnett | 1 | 1 | ||
| Bayes factor analysis | 1 | 1 | ||
| nonparametric mass univariate permutation analysis | 1 | 1 | ||
| paired-samples t test | 1 | 1 | ||
| mass univariate | 1 | 1 | ||
| M | 1 | 1 | ||
| S+L−SL | 1 | 1 | ||
| mathematical model | 1 | 1 | ||
| classification accuracy | 1 | 1 | ||
| train/test split (held-out evaluation) | 1 | 1 | ||
| standardization (mean and variance) | 1 | 1 | ||
| unpaired t-test on morphological measures | 1 | 1 | ||
| proportion of presynaptic partners by source | 1 | 1 | ||
| family wise error | 1 | 1 | ||
| multiple linear regression | 1 | 1 | ||
| ROC / signal detection analysis | 1 | 1 | ||
| support vector machine with held-out test set | 1 | 1 | ||
| Fano factor (variance-to-mean ratio) | 1 | 1 | ||
| Two-way repeated-measures ANOVA | 1 | 1 | ||
| compression index | 1 | 1 | ||
| Monte Carlo simulation (multiple-comparison correction) | 1 | 1 | ||
| paired t -tests | 1 | 1 | ||
| family-wise corrected | 1 | 1 | ||
| Cramér-Rao bound | 1 | 1 | ||
| lognormal distribution | 1 | 1 | ||
| maximum-likelihood | 1 | 1 | ||
| planned contrast | 1 | 1 | ||
| alpha level | 1 | 1 | ||
| information-criterion model comparison (BIC/AIC) | 1 | 1 | ||
| binomial confidence interval | 1 | 1 | ||
| multiple-comparison correction (Bonferroni-Holm) | 1 | 1 | ||
| Markov chain Monte Carlo (Metropolis-Hastings) posterior estimation | 1 | 1 | ||
| Generalized Linear Mixed-Model | 1 | 1 | ||
| type II error | 1 | 1 | ||
| hierarchical Bayesian model | 1 | 1 | ||
| null-hypothesis significance testing | 1 | 1 | ||
| LOESS local regression | 1 | 1 | ||
| receiver operating characteristic (Type II ROC) analysis | 1 | 1 | ||
| coefficient of determination / model fit | 1 | 1 | ||
| test-retest (split-half) reliability | 1 | 1 | ||
| nuisance covariates in a regression model | 1 | 1 | ||
| signal-detection sensitivity and criterion | 1 | 1 | ||
| test-retest reliability | 1 | 1 | ||
| multiple comparisons correction | 1 | 1 | ||
| receiver operating characteristic | 1 | 1 | ||
| signal detection theory | 1 | 1 | ||
| receiver operating characteristic / area under the curve | 1 | 1 | ||
| t-test (paired) | 1 | 1 | ||
| t-test (one-sample) | 1 | 1 | ||
| random-effects group analysis | 1 | 1 | ||
| type 1 signal detection theory | 1 | 1 | ||
| area under the type 2 ROC | 1 | 1 | ||
| psychophysiological interaction | 1 | 1 | ||
| orthogonalisation against reaction time | 1 | 1 | ||
| phase-locked | 1 | 1 | ||
| impedance profile | 1 | 1 | ||
| zero-time cross-correlation | 1 | 1 | ||
| paired Wilcoxon signed rank test | 1 | 1 | ||
| resampling / surrogate data methods | 1 | 1 | ||
| spike-jitter resampling for cross-correlogram significance | 1 | 1 | ||
| kernel density estimation | 1 | 1 | ||
| multiple-comparison correction (family-wise error rate) | 1 | 1 | ||
| randomization by thinning (statistical power control) | 1 | 1 | ||
| jitter | 1 | 1 | ||
| Clopper-Pearson confidence intervals | 1 | 1 | ||
| standard deviation (used as a response threshold) | 1 | 1 | ||
| gaussian kernel smoothing | 1 | 1 | ||
| two-sample t test | 1 | 1 | ||
| Student’s t test | 1 | 1 | ||
| post–stimulus time histogram | 1 | 1 | ||
| sparse principal component analysis | 1 | 1 | ||
| random forest classifier | 1 | 1 | ||
| discriminability index (effect size) | 1 | 1 | ||
| Jensen-Shannon divergence | 1 | 1 | ||
| resampling / subsampling for cluster stability | 1 | 1 | ||
| Mann-Whitney / Wilcoxon rank sum test | 1 | 1 | ||
| least-squares curve fitting | 1 | 1 | ||
| design-based stereological sampling (optical fractionator) | 1 | 1 | ||
| two-way ANOVA with interaction term | 1 | 1 | ||
| alpha level / significance threshold | 1 | 1 | ||
| hierarchical clustering | 1 | 1 | ||
| percent explained variance | 1 | 1 | ||
| CSD | 1 | 1 | ||
| simulations | 1 | 1 | ||
| log-likelihood model comparison | 1 | 1 | ||
| likelihood ratio | 1 | 1 | ||
| smoothing / kernel estimation | 1 | 1 | ||
| Receiver operating characteristic | 1 | 1 | ||
| nonparametric resampling bootstrap | 1 | 1 | ||
| factor analysis / dimensionality reduction | 1 | 1 | ||
| classifier (decoding accuracy) | 1 | 1 | ||
| shuffle / permutation control | 1 | 1 | ||
| Spearman's correlation of pair difference against distance | 1 | 1 | ||
| shuffled dataset control | 1 | 1 | ||
| multiple regression on running parameters | 1 | 1 | ||
| t-test (planned comparisons) | 1 | 1 | ||
| standard deviation (outlier trimming) | 1 | 1 | ||
| interaction test in a mixed factorial design | 1 | 1 | ||
| partial η 2 | 1 | 1 | ||
| within subjects’ ANOVA | 1 | 1 | ||
| Planned comparisons | 1 | 1 | ||
| MSE | 1 | 1 | ||
| mixed design | 1 | 1 | ||
| one tailed | 1 | 1 | ||
| familywise error correction | 1 | 1 | ||
| Rayleigh test (circular uniformity) | 1 | 1 | ||
| Watson-Williams test | 1 | 1 | ||
| permutation tests | 1 | 1 | ||
| Rayleigh tests | 1 | 1 | ||
| Watson–Williams tests | 1 | 1 | ||
| Fisher’s exact test | 1 | 1 | ||
| probit / multinomial discrete choice models | 1 | 1 | ||
| Bayesian Markov chain Monte Carlo estimation | 1 | 1 | ||
| goodness-of-fit test | 1 | 1 | ||
| factor analysis | 1 | 1 | ||
| nonlinear (exponential) curve fitting with model comparison | 1 | 1 | ||
| goodness-of-fit threshold for inclusion | 1 | 1 | ||
| propagation of error | 1 | 1 | ||
| linear interpolation | 1 | 1 | ||
| spectral (gain function) analysis | 1 | 1 | ||
| Wilcoxon ranks sum test | 1 | 1 | ||
| regression coefficient | 1 | 1 | ||
| mixed-design ANOVA | 1 | 1 | ||
| regression / coefficient of determination | 1 | 1 | ||
| lag one regression coefficient | 1 | 1 | ||
| Type I error rate | 1 | 1 | ||
| log transformation (variance homogenisation) | 1 | 1 | ||
| F test across six frequency conditions | 1 | 1 | ||
| cluster thresholding | 1 | 1 | ||
| non-parametric permutation test | 1 | 1 | ||
| between-session correlation coefficient | 1 | 1 | ||
| outlier trimming at 3 SD | 1 | 1 | ||
| sliding-window analysis | 1 | 1 | ||
| maximum likelihood fitting of a logistic psychometric function | 1 | 1 | ||
| rank-sum test | 1 | 1 | ||
| logistic function | 1 | 1 | ||
| multinomial logistic regression | 1 | 1 | ||
| trial shuffling (permutation control) | 1 | 1 | ||
| Hartigan's dip test for bimodality | 1 | 1 | ||
| multinomial logistic regression decoding | 1 | 1 | ||
| trial shuffling | 1 | 1 | ||
| two-way ANOVA interaction | 1 | 1 | ||
| standard score (z score) | 1 | 1 | ||
| surrogate/shuffled data test | 1 | 1 | ||
| paired T-test | 1 | 1 | ||
| correlation coefficients | 1 | 1 | ||
| Z test against zero by distance bin | 1 | 1 | ||
| pairwise Pearson correlation against intersomatic distance | 1 | 1 | ||
| bias correction | 1 | 1 | ||
| Shannon information | 1 | 1 | ||
| bootstrap procedure | 1 | 1 | ||
| limited sampling bias | 1 | 1 | ||
| classifier / decoding accuracy | 1 | 1 | ||
| bootstrap test | 1 | 1 | ||
| F-test / main effect | 1 | 1 | ||
| Huynh-Feldt correction for sphericity | 1 | 1 | ||
| between-subjects factor in mixed ANOVA | 1 | 1 | ||
| model fitting (binomial / logistic psychometric fit) | 1 | 1 | ||
| d’ | 1 | 1 | ||
| t 16 | 1 | 1 | ||
| outlier trimming by standard deviations | 1 | 1 | ||
| multiple comparison correction / type I error rate | 1 | 1 | ||
| ANOVA interaction test | 1 | 1 | ||
| Geisser-Greenhouse sphericity correction | 1 | 1 | ||
| mean and standard error of the mean | 1 | 1 | ||
| z-score normalisation | 1 | 1 | ||
| linear fit (slope estimation by regression line) | 1 | 1 | ||
| significance testing (non-significant trend) | 1 | 1 | ||
| eta squared | 1 | 1 | ||
| F test | 1 | 1 | ||
| Poisson regression | 1 | 1 | ||
| two-factor ANOVA | 1 | 1 | ||
| Pearson’s linear correlation coefficient | 1 | 1 | ||
| multi-voxel pattern analysis | 1 | 1 | ||
| linear support vector machine | 1 | 1 | ||
| one tailed t -tests | 1 | 1 | ||
| Repeated measures ANOVAs | 1 | 1 | ||
| dprimes | 1 | 1 | ||
| small-volume correction | 1 | 1 | ||
| psychophysiological interaction analysis | 1 | 1 | ||
| one-tailed t tests | 1 | 1 | ||
| normality test (Shapiro-Wilk) with t-test | 1 | 1 | ||
| nonparametric rank tests (Mann-Whitney, Kruskal-Wallis, Friedman) | 1 | 1 | ||
| ANOVA with Bonferroni correction | 1 | 1 | ||
| threshold | 1 | 1 | ||
| test of two proportions | 1 | 1 | ||
| relative frequency (normalised word count) | 1 | 1 | ||
| proportions test | 1 | 1 | ||
| G-test | 1 | 1 | ||
| Poisson test with Bonferroni correction | 1 | 1 | ||
| spike distance metric | 1 | 1 | ||
| curve fitting (Gaussian fit, coefficient of determination) | 1 | 1 | ||
| one-way analysis of variance | 1 | 1 | ||
| K–S test | 1 | 1 | ||
| Tamhane | 1 | 1 | ||
| Bootstrap sampling | 1 | 1 | ||
| coordinate-based meta-analysis | 1 | 1 | ||
| permutation test with family-wise error cluster correction | 1 | 1 | ||
| contrast analysis (permutation-based) | 1 | 1 | ||
| meta-analytic connectivity modeling | 1 | 1 | ||
| probability-distribution | 1 | 1 | ||
| averaging of repeated measurements | 1 | 1 | ||
| chance level | 1 | 1 | ||
| lag sequential analysis | 1 | 1 | ||
| z-score (adjusted residual) | 1 | 1 | ||
| chi-squared / model fit indices | 1 | 1 | ||
| bootstrap likelihood ratio test | 1 | 1 | ||
| information criteria (AIC / adjusted BIC) and entropy | 1 | 1 | ||
| latent-profile-analysis | 1 | 1 | ||
| representational similarity analysis with rank correlation | 1 | 1 | ||
| generalized linear mixed-effects model | 1 | 1 | ||
| Wilcoxon signed-rank test with effect size | 1 | 1 | ||
| permutation test with family-wise error correction | 1 | 1 | ||
| Kendall's tau | 1 | 1 | ||
| family-wise error correction | 1 | 1 | ||
| post hoc multiple-comparison correction | 1 | 1 | ||
| psychometric curve | 1 | 1 | ||
| chi-squared expected-cell-frequency assumption | 1 | 1 | ||
| histogram (binned frequency distribution) | 1 | 1 | ||
| smoothing (fast Fourier transform) | 1 | 1 | ||
| curve fitting (sigmoidal function) | 1 | 1 | ||
| logarithmic histogram of best frequencies with peak picking | 1 | 1 | ||
| repeated measurement of the same unit over time | 1 | 1 | ||
| Kaiser criterion for component retention | 1 | 1 | ||
| paired-sample t test | 1 | 1 | ||
| β coefficient | 1 | 1 | ||
| Kaiser criterion | 1 | 1 | ||
| z -transformed | 1 | 1 | ||
| normalized dot product | 1 | 1 | ||
| cluster-based permutation test | 1 | 1 | ||
| Monte-Carlo randomisation null distribution | 1 | 1 | ||
| ANCOVA (nonparametric ranked mixed model) | 1 | 1 | ||
| percentiles / box plot summary | 1 | 1 | ||
| beta-binomial model | 1 | 1 | ||
| effect size (Thurstonian d-prime) | 1 | 1 | ||
| Tukey post hoc test | 1 | 1 | ||
| resampling with replacement | 1 | 1 | ||
| signal detection theory measures (d-prime and criterion) | 1 | 1 | ||
| cross correlation | 1 | 1 | ||
| resampled | 1 | 1 | ||
| planned contrasts (Helmert) | 1 | 1 | ||
| three-way ANOVA | 1 | 1 | ||
| four-way ANOVA with alpha level | 1 | 1 | ||
| post hoc pairwise comparison (Fisher's LSD) | 1 | 1 | ||
| repeated-measures ANOVA with multiple-comparison correction | 1 | 1 | ||
| mean ± s.e.m. | 1 | 1 | ||
| main effect | 1 | 1 | ||
| outlier removal | 1 | 1 | ||
| held-out test set validation | 1 | 1 | ||
| percentiles | 1 | 1 | ||
| Wilcoxon signed paired test | 1 | 1 | ||
| Wilcoxon-Mann-Whitney test | 1 | 1 | ||
| support vector machine (SVM) classifier | 1 | 1 | ||
| unpaired t-test | 1 | 1 | ||
| unpaired t | 1 | 1 | ||
| Newman-Keuls | 1 | 1 | ||
| Pearson product-moment correlation | 1 | 1 | ||
| Z -score | 1 | 1 | ||
| curve fitting (Gaussian) | 1 | 1 | ||
| goodness of fit / coefficient of determination | 1 | 1 | ||
| resampling (random subsampling of trials) | 1 | 1 | ||
| parametric model fitting | 1 | 1 | ||
| two-sample Kolmogorov–Smirnov test | 1 | 1 | ||
| multiple regression analysis | 1 | 1 | ||
| difference error in octaves | 1 | 1 | ||
| SD of BF prediction errors | 1 | 1 | ||
| chi-squared / Fisher's exact test | 1 | 1 | ||
| spectral power analysis (Fast Fourier Transform) | 1 | 1 | ||
| t | 1 | 1 | ||
| day effect | 1 | 1 | ||
| Fast Fourier Transform | 1 | 1 | ||
| curve fitting (exponential) | 1 | 1 | ||
| curve fitting (sigmoid) | 1 | 1 | ||
| quantile binning | 1 | 1 | ||
| Kolmogorov–Smirnov test | 1 | 1 | ||
| Wilcoxon signed-rank / rank-sum test | 1 | 1 | ||
| one-tailed test | 1 | 1 | ||
| Shapiro–Wilk normality test | 1 | 1 | ||
| Levene test for equality of variance | 1 | 1 | ||
| Kruskal–Wallis test with Dunn's post hoc | 1 | 1 | ||
| one-way ANOVA with Tukey post hoc | 1 | 1 | ||
| curve fitting (single exponential) | 1 | 1 | ||
| curve fitting (Boltzmann function) | 1 | 1 | ||
| curve fitting (double exponential) | 1 | 1 | ||
| nonlinear least squares curve fitting | 1 | 1 | ||
| randomization (permutation) control | 1 | 1 | ||
| unpaired t tests | 1 | 1 | ||
| Wilcoxon rank sum | 1 | 1 | ||
| weighted linear regression | 1 | 1 | ||
| nonlinear least squares | 1 | 1 | ||
| t-test (sample-by-sample, time-resolved) | 1 | 1 | ||
| ANOVA (two-way) | 1 | 1 | ||
| ANOVA (three-way) with Bonferroni correction for multiple comparisons | 1 | 1 | ||
| linear discriminant analysis with principal component analysis | 1 | 1 | ||
| sample-by-sample t -test | 1 | 1 | ||
| Monte Carlo permutations | 1 | 1 | ||
| Bonferroni confidence interval adjustment | 1 | 1 | ||
| vector strength (circular phase-locking measure) | 1 | 1 | ||
| paired Wilcoxon signed-rank test | 1 | 1 | ||
| Two-way ANOVA test | 1 | 1 | ||
| vector strength | 1 | 1 | ||
| effect size / sensitivity index | 1 | 1 | ||
| Wilcoxon rank-sum | 1 | 1 | ||
| Poisson | 1 | 1 | ||
| D′ | 1 | 1 | ||
| Tukey's HSD post hoc test | 1 | 1 | ||
| Levene's test for homogeneity of variance | 1 | 1 | ||
| least-squares curve fitting (chi-squared minimisation) | 1 | 1 | ||
| F statistic | 1 | 1 | ||
| Mahalanobis distance (spike-cluster quality metric) | 1 | 1 | ||
| kurtosis of the firing rate distribution | 1 | 1 | ||
| regression (least squares) | 1 | 1 | ||
| unbiased stereological sampling | 1 | 1 | ||
| s.e.m. | 1 | 1 | ||
| ANOVA (F-test) | 1 | 1 | ||
| Cohen's kappa (interrater reliability) | 1 | 1 | ||
| p-rep (probability of replication) | 1 | 1 | ||
| correlation (Pearson) | 1 | 1 | ||
| correlation (Spearman rank) | 1 | 1 | ||
| Dice coefficient (spatial overlap) | 1 | 1 | ||
| representational similarity analysis | 1 | 1 | ||
| Wilcoxon signed-rank tests | 1 | 1 | ||
| FDR corrected | 1 | 1 | ||
| covariance matrix estimation | 1 | 1 | ||
| Gaussian distribution | 1 | 1 | ||
| Fisher's combined probability test | 1 | 1 | ||
| permutation (shuffle) test | 1 | 1 | ||
| bias correction of an estimator | 1 | 1 | ||
| mutual information between successive time windows | 1 | 1 | ||
| shuffling across repetitions | 1 | 1 | ||
| Fisher's combined test | 1 | 1 | ||
| marginal and conditional entropy decomposition | 1 | 1 | ||
| bifurcation diagram | 1 | 1 | ||
| clustered nonparametric test | 1 | 1 | ||
| clustered Wilcoxon rank-sum test | 1 | 1 | ||
| clustered Wilcoxon signed-rank test | 1 | 1 | ||
| nonlinear least-squares curve fitting | 1 | 1 | ||
| isolation distance and L-ratio | 1 | 1 | ||
| signal-detection sensitivity | 1 | 1 | ||
| cluster mass permutation tests | 1 | 1 | ||
| forgetting curves | 1 | 1 | ||
| near chance levels | 1 | 1 | ||
| mean (signal averaging across trials) | 1 | 1 | ||
| k-fold cross-validation | 1 | 1 | ||
| regularized regression (LASSO / L1) | 1 | 1 | ||
| held-out test set | 1 | 1 | ||
| LASSO regularization | 1 | 1 | ||
| CC norm | 1 | 1 | ||
| support vector machine classifier | 1 | 1 | ||
| generalized linear model regression | 1 | 1 | ||
| maximum likelihood estimation (psychometric function fitting) | 1 | 1 | ||
| bootstrap aggregation (bagging) | 1 | 1 | ||
| PAIRS | 1 | 1 | ||
| SVM | 1 | 1 | ||
| p-values from Monte Carlo simulations | 1 | 1 | ||
| weighted mean | 1 | 1 | ||
| bootstrapping instead of spatial blurring | 1 | 1 | ||
| linear fit of centroid frequency against depth | 1 | 1 | ||
| bootstrapped confidence intervals on centroid frequency | 1 | 1 | ||
| kernel smoothing | 1 | 1 | ||
| clustering | 1 | 1 | ||
| paired sign test | 1 | 1 | ||
| resampling | 1 | 1 | ||
| cross-validated decoding accuracy | 1 | 1 | ||
| number of implementable binary classifications | 1 | 1 | ||
| permutation / null-distribution test | 1 | 1 | ||
| FDR correction | 1 | 1 | ||
| z ‐scores | 1 | 1 | ||
| two-sample t-test | 1 | 1 | ||
| robust estimator (median absolute deviation) | 1 | 1 | ||
| maximum likelihood estimation (mixture of Gaussians) | 1 | 1 | ||
| false discovery rate / true positive rate | 1 | 1 | ||
| Fowlkes-Mallows index (clustering agreement) | 1 | 1 | ||
| mixture of Gaussians | 1 | 1 | ||
| ROC curve | 1 | 1 | ||
| Fowlkes-Mallows index | 1 | 1 | ||
| geometric random walk | 1 | 1 | ||
| prior distribution | 1 | 1 | ||
| binomial probability | 1 | 1 | ||
| double ISH | 1 | 1 | ||
| ANOVA for repeated measures | 1 | 1 | ||
| Greenhouse-Geisser | 1 | 1 | ||
| paired t test (two-tailed) | 1 | 1 | ||
| alpha level of 0.05 | 1 | 1 | ||
| post-hoc uncorrected comparisons | 1 | 1 | ||
| stimulus×time-segment -interaction | 1 | 1 | ||
| activation likelihood estimation meta-analysis | 1 | 1 | ||
| laterality index | 1 | 1 | ||
| activation likelihood estimated (ALE) | 1 | 1 | ||
| laterality indexes | 1 | 1 | ||
| entropy | 1 | 1 | ||
| explained variance (r-squared) | 1 | 1 | ||
| k -means clustering | 1 | 1 | ||
| F-test / ANOVA | 1 | 1 | ||
| jackknife | 1 | 1 | ||
| data smoothing filter | 1 | 1 | ||
| percentile threshold | 1 | 1 | ||
| linear regression / slope | 1 | 1 | ||
| linearly related | 1 | 1 | ||
| Mann-Whitney test (one-tailed) | 1 | 1 | ||
| z score | 1 | 1 | ||
| G-test of independence | 1 | 1 | ||
| g-test | 1 | 1 | ||
| rank-sum | 1 | 1 | ||
| sign-rank | 1 | 1 | ||
| nonparametric multiple-comparison correction | 1 | 1 | ||
| spectral power density estimation (Welch periodogram) | 1 | 1 | ||
| F-test for interaction | 1 | 1 | ||
| Kruskal-Wallis one-way ANOVA on ranks | 1 | 1 | ||
| two-way ANOVA (nonparametric) | 1 | 1 | ||
| likelihood-ratio (G) test of independence | 1 | 1 | ||
| all descriptive statistics (n, median, IQR, mean, SEM) | 1 | 1 | ||
| Non-parametric test | 1 | 1 | ||
| goodness-of-fit measure | 1 | 1 | ||
| model fitting | 1 | 1 | ||
| signal detection theory model | 1 | 1 | ||
| error of prediction | 1 | 1 | ||
| binomial test with Bonferroni correction | 1 | 1 | ||
| Mann-Whitney U test / Wilcoxon signed-rank test | 1 | 1 | ||
| likelihood-ratio (G) test | 1 | 1 | ||
| Wheeler-Watson two-sample test | 1 | 1 | ||
| t-SNE dimensionality reduction | 1 | 1 | ||
| cross-correlograms | 1 | 1 | ||
| statistical variability | 1 | 1 | ||
| cluster | 1 | 1 | ||
| firing probability | 1 | 1 | ||
| Rayleigh test of circular uniformity | 1 | 1 | ||
| pairwise phase consistency (spike-field phase-locking measure) | 1 | 1 | ||
| linear classifier / population decoding | 1 | 1 | ||
| resampling with replacement (bootstrap) | 1 | 1 | ||
| statistical power analysis / effect size | 1 | 1 | ||
| maximum margin linear classifier, cross-validated | 1 | 1 | ||
| pairwise phase consistency | 1 | 1 | ||
| Rayleigh's test | 1 | 1 | ||
| post-hoc power analysis | 1 | 1 | ||
| bootstrap (resampling with replacement) | 1 | 1 | ||
| F1 score (detection performance combining false positives and false negatives) | 1 | 1 | ||
| linear regression (slope of best fit) | 1 | 1 | ||
| log-normal distribution (used to draw simulated connection strengths) | 1 | 1 | ||
| Wilcoxon’s paired signed-rank test | 1 | 1 | ||
| goodness of fit | 1 | 1 | ||
| U test | 1 | 1 | ||
| r 2 | 1 | 1 | ||
| L ratio | 1 | 1 | ||
| F-test with Bonferroni correction | 1 | 1 | ||
| Wilcoxon's paired signed-rank test | 1 | 1 | ||
| exact Binomial test | 1 | 1 | ||
| nested ANOVA | 1 | 1 | ||
| canonical correlation analysis | 1 | 1 | ||
| ROC / Mann-Whitney U statistic | 1 | 1 | ||
| linear classification | 1 | 1 | ||
| Shared Variance Component Analysis | 1 | 1 | ||
| reduced rank regression | 1 | 1 | ||
| power law | 1 | 1 | ||
| two-sided Wilcoxon sign-rank test | 1 | 1 | ||
| spike-field coherence | 1 | 1 | ||
| weighted least squares estimation | 1 | 1 | ||
| model fit indices (RMSEA, CFI, TLI) | 1 | 1 | ||
| composite reliability | 1 | 1 | ||
| relative importance index | 1 | 1 | ||
| oblique factor rotation | 1 | 1 | ||
| eigen-decomposition of the covariance matrix | 1 | 1 | ||
| multiple regression analysis of CF on 3D position | 1 | 1 | ||
| error magnitude between predicted and measured CF | 1 | 1 | ||
| Bayesian ANOVA | 1 | 1 | ||
| evidence for the null hypothesis | 1 | 1 | ||
| d-prime (sensitivity index) | 1 | 1 | ||
| two-alternative forced choice d-prime | 1 | 1 | ||
| cross-validation (leave-one-trial-out) | 1 | 1 | ||
| resampling / shuffled null distribution | 1 | 1 | ||
| trend analysis | 1 | 1 | ||
| rmANOVA | 1 | 1 | ||
| adjusted FDR | 1 | 1 | ||
| z -score | 1 | 1 | ||
| Tukey HSD post hoc test | 1 | 1 | ||
| repeated measures ANOVA with Tukey HSD | 1 | 1 | ||
| additive model of sum versus simultaneous | 1 | 1 | ||
| partial eta-squared | 1 | 1 | ||
| linear-log model | 1 | 1 | ||
| significance level / non-parametric testing | 1 | 1 | ||
| non-parametric | 1 | 1 | ||
| signal detection theory sensitivity measure | 1 | 1 | ||
| mean, standard deviation and median | 1 | 1 | ||
| median and interquartile range (box plot) | 1 | 1 | ||
| cluster analysis (automatic spike sorting) | 1 | 1 | ||
| Wilcoxon rank-sum test in 50 ms bins | 1 | 1 | ||
| Benjamini-Hochberg false discovery rate | 1 | 1 | ||
| four a-priori bin sizes with a disjunctive criterion | 1 | 1 | ||
| median number of spikes | 1 | 1 | ||
| 4(region) × 4(category) χ 2 analysis | 1 | 1 | ||
| percentile | 1 | 1 | ||
| Gaussian function fit | 1 | 1 | ||
| relative fluorescence change | 1 | 1 | ||
| variational Bayesian inference | 1 | 1 | ||
| parameter fitting (grid search) | 1 | 1 | ||
| cross-correlations | 1 | 1 | ||
| least-squares regression | 1 | 1 | ||
| Gaussian distribution fit | 1 | 1 | ||
| variance explained | 1 | 1 | ||
| Kruskal-Wallis one-way ANOVA by ranks | 1 | 1 | ||
| Kruskal–Wallis one-way ANOVA by ranks test | 1 | 1 | ||
| two-sided Wilcoxon signed rank test | 1 | 1 | ||
| Bonferroni–Holm correction | 1 | 1 | ||
| Gaussian Mixture Model | 1 | 1 | ||
| Binomial test | 1 | 1 | ||
| structural equation modelling (partial least squares) | 1 | 1 | ||
| composite reliability and average variance extracted | 1 | 1 | ||
| variance inflation factor (multicollinearity check) | 1 | 1 | ||
| multi-group comparison test | 1 | 1 | ||
| goodness-of-fit residual index | 1 | 1 | ||
| one-way ANOVA with post hoc test | 1 | 1 | ||
| normality and homogeneity-of-variance tests | 1 | 1 | ||
| confidence level | 1 | 1 | ||
| upper bound | 1 | 1 | ||
| eta squared (effect size) | 1 | 1 | ||
| Tukey post hoc correction | 1 | 1 | ||
| effect size (Cohen's d) with confidence interval | 1 | 1 | ||
| Cronbach's alpha (internal consistency) | 1 | 1 | ||
| McDonald's omega (internal consistency) | 1 | 1 | ||
| mediation / path analysis with maximum likelihood and robust standard errors | 1 | 1 | ||
| Monte Carlo simulation power analysis | 1 | 1 | ||
| Mahalanobis distance (outlier exclusion) | 1 | 1 | ||
| receiver operating characteristic (ROC) analysis | 1 | 1 | ||
| signal detection model fit (unequal variance model) | 1 | 1 | ||
| receiver operating characteristics | 1 | 1 | ||
| spline regression smoothing | 1 | 1 | ||
| permutation / shuffle control | 1 | 1 | ||
| wavelet spectral decomposition | 1 | 1 | ||
| Holm-Bonferroni | 1 | 1 | ||
| percentage agreement between two methods | 1 | 1 | ||
| weighted directional connectivity matrices | 1 | 1 | ||
| clustering algorithm | 1 | 1 | ||
| semi-quantitative densities | 1 | 1 | ||
| linear regression (ordinary least squares) | 1 | 1 | ||
| mean squared error minimisation | 1 | 1 | ||
| logistic regression model | 1 | 1 | ||
| signed-rank test | 1 | 1 | ||
| ideal observer | 1 | 1 | ||
| slope of the psychometric curve | 1 | 1 | ||
| Sidak multiple-comparisons correction | 1 | 1 | ||
| Gaussian mixture model clustering with Bayesian Information Criterion model selection | 1 | 1 | ||
| K-nearest-neighbors classification (decoding) | 1 | 1 | ||
| polynomial regression | 1 | 1 | ||
| interquartile range | 1 | 1 | ||
| permutation/shuffle test | 1 | 1 | ||
| lifetime sparseness | 1 | 1 | ||
| fast Fourier transform | 1 | 1 | ||
| phase locking | 1 | 1 | ||
| one-tailed t-test | 1 | 1 | ||
| multivariate autoregressive model order selection by Akaike Information Criterion | 1 | 1 | ||
| penalized spline regression | 1 | 1 | ||
| standard deviation and median absolute deviation | 1 | 1 | ||
| confidence bands | 1 | 1 | ||
| inter-rater reliability (weighted kappa) | 1 | 1 | ||
| Tukey post-hoc test | 1 | 1 | ||
| Bartlett test of homogeneity of variance | 1 | 1 | ||
| null hypothesis testing | 1 | 1 | ||
| descriptive and inferential statistics | 1 | 1 | ||
| Wilks' Lambda | 1 | 1 | ||
| biserial correlation | 1 | 1 | ||
| split-half reliability | 1 | 1 | ||
| hierarchical regression | 1 | 1 | ||
| variance partitioning | 1 | 1 | ||
| Mahalanobis distance (outlier detection) | 1 | 1 | ||
| multiple imputation | 1 | 1 | ||
| structural equation modeling (PLS-SEM) | 1 | 1 | ||
| Heterotrait-Monotrait ratio | 1 | 1 | ||
| Harman's single-factor test | 1 | 1 | ||
| cross-validation (blindfolding predictive relevance) | 1 | 1 | ||
| inter-rater agreement | 1 | 1 | ||
| spectral coherence (multi-taper estimate) | 1 | 1 | ||
| linear regression with R-squared | 1 | 1 | ||
| U-test | 1 | 1 | ||
| t-test with false-discovery-rate correction | 1 | 1 | ||
| correlation against shuffled data with Bonferroni correction | 1 | 1 | ||
| linear decoder (classification analysis) | 1 | 1 | ||
| unsupervised clustering | 1 | 1 | ||
| held-out test data (out-of-sample validation) | 1 | 1 | ||
| two-tailed t test | 1 | 1 | ||
| false discovery rate (FDR) correction | 1 | 1 | ||
| Wilcoxon signed-rank test and Mann-Whitney U-test | 1 | 1 | ||
| F statistic with 95% confidence interval | 1 | 1 | ||
| support vector machine with cross-validation | 1 | 1 | ||
| label-shuffling permutation test | 1 | 1 | ||
| ordered probit regression | 1 | 1 | ||
| robust standard errors | 1 | 1 | ||
| locally weighted regression (lowess) | 1 | 1 | ||
| chi-squared goodness-of-fit test | 1 | 1 | ||
| interquartile range (outlier rule) | 1 | 1 | ||
| planned comparison | 1 | 1 | ||
| d' (sensitivity index) | 1 | 1 | ||
| A' (nonparametric sensitivity measure) | 1 | 1 | ||
| beta (likelihood-ratio response bias measure) | 1 | 1 | ||
| regression (ordinary least squares) | 1 | 1 | ||
| ROC curve fitting (slope and intercept in z space) | 1 | 1 | ||
| model fitting / model comparison | 1 | 1 | ||
| effect size (Cohen's dz) | 1 | 1 | ||
| Granger causality | 1 | 1 | ||
| standardized residuals | 1 | 1 | ||
| Levene's test | 1 | 1 | ||
| maximum likelihood classification | 1 | 1 | ||
| t-statistic | 1 | 1 | ||
| cell counting (stereological-style counts per section) | 1 | 1 | ||
| standard deviation threshold criterion | 1 | 1 | ||
| Cochran's Q test | 1 | 1 | ||
| Newman–Keuls comparison test | 1 | 1 | ||
| Cochran Q test | 1 | 1 | ||
| post hoc multiple comparison (Tukey's test) | 1 | 1 | ||
| one-way repeated-measures ANOVA with Tukey's test | 1 | 1 | ||
| coefficient of variation across step cycles | 1 | 1 | ||
| conditional probability | 1 | 1 | ||
| regression (polynomial curve fitting) | 1 | 1 | ||
| error bars (one standard deviation) | 1 | 1 | ||
| difference between group means | 1 | 1 | ||
| standardized regression coefficient | 1 | 1 | ||
| ordinary least squares regression and probit | 1 | 1 | ||
| multinomial logit regression | 1 | 1 | ||
| null hypothesis significance test / minimum detectable effect | 1 | 1 | ||
| standardization by standard deviations from the mean | 1 | 1 | ||
| fixed effects | 1 | 1 | ||
| predicted probability | 1 | 1 | ||
| mixed-effects logistic regression | 1 | 1 | ||
| mixed-effects linear regression | 1 | 1 | ||
| multidimensional scaling | 1 | 1 | ||
| text mining (word-frequency analysis in R) | 1 | 1 | ||
| t-test (Student's) | 1 | 1 | ||
| Bayesian Markov chain Monte Carlo | 1 | 1 | ||
| multiple-comparison correction (Bonferroni) | 1 | 1 | ||
| gamma-distributed among-site rate variation | 1 | 1 | ||
| dN/dS | 1 | 1 | ||
| Markov chain Monte Carlo | 1 | 1 | ||
| Anderson-Darling test | 1 | 1 | ||
| Kolmogorov-Smirnov test of normality | 1 | 1 | ||
| skewness and kurtosis | 1 | 1 | ||
| normalization | 1 | 1 | ||
| null distribution by label shuffling | 1 | 1 | ||
| median and percentiles | 1 | 1 | ||
| proportion of variance explained | 1 | 1 | ||
| log2(TPM + 1) | 1 | 1 | ||
| hazard ratio in log10 scale | 1 | 1 | ||
| P = 0.022 | 1 | 1 | ||
| P = 0.15 | 1 | 1 | ||
| Quantile-normalization | 1 | 1 | ||
| conservation of total relevance across layers | 1 | 1 | ||
| an order of magnitude between expected and data-calibrated microplastic counts | 1 | 1 | ||
| same order of magnitude agreement with independent estimates | 1 | 1 | ||
| standard deviations of lattice spacings between basal planes | 1 | 1 | ||
| power-law improvement of test loss with data | 1 | 1 | ||
| precision of stable predictions, reported as a hit rate | 1 | 1 | ||
| item and person separation reliability from Rasch | 1 | 1 | ||
| infit and outfit mean square | 1 | 1 | ||
| geometric mean of a log-transformed, survey-weighted distribution | 1 | 1 | ||
| backward elimination at p < 0.05 with Satterwaite adjusted F statistics | 1 | 1 | ||
| detection frequency threshold of 60% for parametric statistics | 1 | 1 | ||
| correlational association between conspiracy belief and analytic thinking | 1 | 1 | ||
| strain-hardening exponent | 1 | 1 | ||
| average precision (AP) | 1 | 1 | ||
| arithmetic mean | 1 | 1 | ||
| Pearson correlation coefficients | 1 | 1 | ||
| Spearman Rho correlations | 1 | 1 | ||
| CVs | 1 | 1 | ||
| uncertainty ranges | 1 | 1 | ||
| uncertainty range | 1 | 1 | ||
| order of magnitude upper bound | 1 | 1 | ||
| general linear F test | 1 | 1 | ||
| type 1 error rate | 1 | 1 | ||
| Fisher’s exact tests | 1 | 1 | ||
| Sensitivity or Recall | 1 | 1 | ||
| Specificity | 1 | 1 | ||
| Pearson product-moment test | 1 | 1 | ||
| r=0.4132 | 1 | 1 | ||
| r=0.99 | 1 | 1 | ||
| reliability | 1 | 1 | ||
| generalizability | 1 | 1 | ||
| SIR | 1 | 1 | ||
| R2 = 0.74 | 1 | 1 | ||
| R2 = 0.98 | 1 | 1 | ||
| negative binomial | 1 | 1 | ||
| kernel bandwidth | 1 | 1 | ||
| ssGSEA | 1 | 1 | ||
| PPV | 1 | 1 | ||
| R2 | 1 | 1 | ||
| SE | 1 | 1 | ||
| Rietveld refinement | 1 | 1 | ||
| DSC | 1 | 1 | ||
| p | 1 | 1 | ||
| Cohen | 1 | 1 | ||
| quasi-Poisson | 1 | 1 | ||
| CI | 1 | 1 | ||
| Monte Carlo | 1 | 1 | ||
| VC dimension | 1 | 1 | ||
| PSS | 1 | 1 | ||
| mAP | 1 | 1 | ||
| IOU | 1 | 1 | ||
| R0 | 1 | 1 | ||
| normal range | 1 | 1 | ||
| density | 1 | 1 | ||
| ratio | 1 | 1 | ||
| profile | 1 | 1 | ||
| contrastive divergence | 1 | 1 | ||
| probability distribution | 1 | 1 | ||
| estimated | 1 | 1 | ||
| sparse regression | 1 | 1 | ||
| capacity | 1 | 1 | ||
| quantify | 1 | 1 | ||
| semi-quantitative | 1 | 1 | ||
| affinity | 1 | 1 | ||
| average | 1 | 1 | ||
| content analysis | 1 | 1 | ||
| false positive rate | 1 | 1 | ||
| Statistical estimates | 1 | 1 | ||
| average precision | 1 | 1 | ||
| similarity | 1 | 1 | ||
| expression profiling | 1 | 1 | ||
| posterior SD | 1 | 1 | ||
| selectivity | 1 | 1 | ||
| deconvolution | 1 | 1 | ||
| Cronbach | 1 | 1 | ||
| 95%CI | 1 | 1 | ||
| Kendall rank correlation coefficient | 1 | 1 | ||
| minimum spanning tree | 1 | 1 | ||
| MACS2 | 1 | 1 | ||
| false positive rates | 1 | 1 | ||
| positive predictive values | 1 | 1 | ||
| measurement equivalence | 1 | 1 | ||
| concurrent validity | 1 | 1 | ||
| limits of quantification | 1 | 1 | ||
| quality control metric | 1 | 1 | ||
| specificity score | 1 | 1 | ||
| 95% confidence limits | 1 | 1 | ||
| F1 score | 1 | 1 | ||
| precision and recall | 1 | 1 | ||
| multivariate normal methods | 1 | 1 | ||
| 27.9% versus 21.9%, P = 0.020 | 1 | 1 | ||
| 92·7% (95% CI 90·2–94·8) | 1 | 1 | ||
| 1.53 vs. 1.73; 1.69 vs. 1.71; 1.37 vs. 1.23 | 1 | 1 | ||
| percentage of studies | 1 | 1 | ||
| additive model | 1 | 1 | ||
| correlation between two well-being measures | 1 | 1 | ||
| share of a sample attributable to a disputed member | 1 | 1 | ||
| count of congeners | 1 | 1 | ||
| correlation coefficient between half data sets | 1 | 1 | ||
| intra-class correlation coefficient | 1 | 1 | ||
| sufficiency without a level | 1 | 1 | ||
| the only sensitivity and specificity in the paper belong to the SUPPLANTED term | 1 | 1 | ||
| the deadly-sins triad as a checklist for a construct | 1 | 1 | ||
| a significance test applied to a DEFINITIONAL process | 1 | 1 | ||
| an average whose spread is four times the effect it reports | 1 | 1 | ||
| a majority treated as a failure by rank rather than by threshold | 1 | 1 | ||
| a class of study defined so that it cannot meet its journals' criterion | 1 | 1 | ||
| a diversity index and a taxon count disagreeing on the same sample | 1 | 1 | ||
| a label that predicts opposite outcomes inside itself | 1 | 1 | ||
| a construct whose dose-response is a step, not a slope | 1 | 1 | ||
| a precision measured against text, not against the experts | 1 | 1 | ||
| an error rate whose denominator its own authors dispute | 1 | 1 | ||
| a species boundary that depends on which program computed it | 1 | 1 | ||
| an order of magnitude, against a known answer | 1 | 1 | ||
| 90% is a best case reported as a norm | 1 | 1 | ||
| sensitivity and specificity as the currency of every revision | 1 | 1 | ||
| sensitivity 1.00 and specificity 0.99, against a standard built from the same twelve items | 1 | 1 | ||
| three kappas doing three different jobs | 1 | 1 | ||
| discriminant validity is reported as phi coefficients | 1 | 1 | ||
| mean plus two standard deviations, doing the work of a disease boundary | 1 | 1 | ||
| hazard ratios as the argument for moving a threshold | 1 | 1 | ||
| 36% of the borderline band already meet the new resistance criterion | 1 | 1 | ||
| a positive predictive value that moves 30 points with the population | 1 | 1 | ||
| a diagnostic threshold that was a commercial parameter | 1 | 1 | ||
| sensitivity degraded by the standard of care | 1 | 1 | ||
| a framework with no statistics at all | 1 | 1 | ||
| the moderator with a measured effect of zero studies | 1 | 1 | ||
| a negative result published against the authors' own threshold | 1 | 1 | ||
| two Methods assumptions that both push toward discreteness | 1 | 1 | ||
| a percentage that is a property of sampling, not of nature | 1 | 1 | ||
| a validation chain with no true value at the end of it | 1 | 1 | ||
| what the standard toolbox silently does not cover | 1 | 1 | ||
| a test for difference that is underpowered by construction | 1 | 1 | ||
| wide intervals are not a null result | 1 | 1 | ||
| a definitional difference reported as a number instead of a caveat | 1 | 1 | ||
| every historical comparison names the criteria it holds fixed | 1 | 1 | ||
| two biases, one direction | 1 | 1 | ||
| a definitional procedure with a confusion matrix | 1 | 1 | ||
| a criterion satisfiable by the wrong process | 1 | 1 | ||
| evidence for the components, not for the conjunction | 1 | 1 | ||
| thresholds selected by review rather than derived | 1 | 1 | ||
| a sample size that looks decided and is conventional | 1 | 1 | ||
| a method with no measure, stated as such | 1 | 1 | ||
| a frequency table over a category set the same paper calls unstable | 1 | 1 | ||
| a review with no effect size, and it says so | 1 | 1 | ||
| a taxonomy with no prevalence, and it says so twice | 1 | 1 | ||
| a bias and a power loss that are the same property seen twice | 1 | 1 | ||
| a review whose judgements are readings, and it does not pretend otherwise | 1 | 1 | ||
| P < 0.001 | 1 | 1 | ||
| P < 0.05 | 1 | 1 | ||
| p=0.001 | 1 | 1 | ||
| P = 0.001 | 1 | 1 | ||
| P = 0.05 | 1 | 1 | ||
| p = 0.775 | 1 | 1 | ||
| p < .05 | 1 | 1 | ||
| P = 0.002 | 1 | 1 | ||
| P = 0.478 | 1 | 1 | ||
| Logistic regression | 1 | 1 | ||
| p = .057 | 1 | 1 | ||
| fold change and P value | 1 | 1 | ||
| Multivariate logistic regression was performed to identify the influencing factors of vaccination acceptance. | 1 | 1 | ||
| Multivariate logistic regression was then performed between the vaccine demand group and vaccine delay group to identify the influencing factors of vaccination acceptance (immediate or delayed acceptance), with the odds ratio (OR), standard error (SE) and a 95% confidence interval (CI) being calculated. | 1 | 1 | ||
| Besides, Table S2 presents the results of regression, including significant factors at the 10% level of chi-squared tests. | 1 | 1 | ||
| Logistic regression showed that being male, being married, perceiving a high risk of infection, being vaccinated against influenza in the past season, believing in the efficacy of COVID-19 vaccination or valuing doctor’s recommendations could increase the probability of accepting COVID-19 vaccination as soon as possible, while having confirmed or suspected cases in local areas, valuing vaccination convenience or vaccine price in decision-making could hinder participants from immediate vaccination. | 1 | 1 | ||
| sex interaction | 1 | 1 | ||
| age interaction | 1 | 1 | ||
| Study 1 belief–behaviour association | 1 | 1 | ||
| Study 2 belief–behaviour association | 1 | 1 | ||
| Study 3 social-media–belief association | 1 | 1 | ||
| Study 3 belief–behaviour association | 1 | 1 | ||
| odds ratios and confidence intervals | 1 | 1 | ||
| infection-risk perception association | 1 | 1 | ||
| vaccine-confidence association | 1 | 1 | ||
| information-period association | 1 | 1 | ||
| ethnicity and education odds ratios | 1 | 1 | ||
| gender association | 1 | 1 | ||
| age-group associations | 1 | 1 | ||
| education gradient | 1 | 1 | ||
| overall vaccine-measure time trends | 1 | 1 | ||
| party-specific time trends | 1 | 1 | ||
| party-specific vaccination time trend | 1 | 1 | ||
| Republican flu-shot trend | 1 | 1 | ||
| party-strength interaction | 1 | 1 | ||
| institutional-trust reliability | 1 | 1 | ||
| perceived-threat reliability | 1 | 1 | ||
| country differences in COVID-19 vaccine acceptance | 1 | 1 | ||
| information-source differences in VCBS | 1 | 1 | ||
| information source and origin-conspiracy belief | 1 | 1 | ||
| information source and vaccine willingness | 1 | 1 | ||
| age-category comparison | 1 | 1 | ||
| prior probability of pathogenicity | 1 | 1 | ||
| predictor-score correlations | 1 | 1 | ||
| default-threshold predicted-pathogenic fractions | 1 | 1 | ||
| interval uncertainty | 1 | 1 | ||
| evidence-strength frequency | 1 | 1 | ||
| return distribution comparison | 1 | 1 | ||
| robustness confidence level | 1 | 1 | ||
| risk-measure robustness | 1 | 1 | ||
| vaccine-belief SEM fit | 1 | 1 | ||
| convergent validity correlation | 1 | 1 | ||
| demographic interactions | 1 | 1 | ||
| factor-hesitancy associations | 1 | 1 | ||
| vaccine-belief effect | 1 | 1 | ||
| mistrust-healthcare SEM fit | 1 | 1 | ||
| AGO2 cluster-density comparison | 1 | 1 | ||
| ribosome-profiling result | 1 | 1 | ||
| super-spreader bot scores | 1 | 1 | ||
| circNRIP1 overall survival | 1 | 1 | ||
| circNRIP1 disease-free survival | 1 | 1 | ||
| miR-149-5p overall survival | 1 | 1 | ||
| Kuder-Richardson 20 | 1 | 1 | ||
| Guttman split-half reliability | 1 | 1 | ||
| Dice coefficient | 1 | 1 | ||
| intraclass correlation coefficient | 1 | 1 | ||
| F1-score | 1 | 1 | ||
| Youden's J statistic | 1 | 1 | ||
| area under the ROC curve | 1 | 1 | ||
| switch error rate | 1 | 1 | ||
| pair-correlation function | 1 | 1 | ||
| selection differential | 1 | 1 | ||
| Jaccard index | 1 | 1 | ||
| root mean squared deviation | 1 | 1 | ||
| Area Under the Curve | 1 | 1 |
Each row is a different discipline; the same statistical join key connects them — a candidate cross-field neighbourhood the free-text layer could not surface.
| Paper (a different field each row) | verbatim evidence | |
|---|---|---|
| • | CAN: Creative Adversarial Networks, Generating Art by Learning About Styles an art-history · 2017 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | Language Models are Few-Shot Learners artificial-intelligence · 2020 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | Observation of Gravitational Waves from a Binary Black Hole Merger astronomy · 2016 | “The scales on the top give the significance of an event in Gaussian standard deviations based on the corresponding noise background.” |
| • | Comparison of Three Nudge Interventions (Priming, Default Option, and Perceive behavioral-economics · 2017 | “3 Mean ± SD, all such values.” |
| • | The Large-Scale Organization of Metabolic Networks biology · 2000 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | Automatic Chemical Design Using a Data-Driven Continuous Representation of Mol chemistry · 2018 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | Studies of Interference in Serial Verbal Reactions cognitive-psychology · 1935 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | SynthSeg: Segmentation of brain MRI scans of any contrast and resolution witho computer-science · 2023 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| Paper (a different field each row) | verbatim evidence | |
|---|---|---|
| • | CAN: Creative Adversarial Networks, Generating Art by Learning About Styles an art-history · 2017 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | Language Models are Few-Shot Learners artificial-intelligence · 2020 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | Planck 2018 Results. VI. Cosmological Parameters astronomy · 2020 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | Can Priming Cooperation Increase Public Good Contributions? behavioral-economics · 2015 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | xCell: digitally portraying the tissue cellular heterogeneity landscape biology · 2017 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | Frequency and Repetition Effects in Lexical Memory cognitive-psychology · 1977 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddin digital-humanities · 2019 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | Quantifying the Consensus on Anthropogenic Global Warming in the Scientific Li earth-science · 2013 | “Over time, the no position percentage has increased (simple linear regression trend 0.87% ± 0.28% yr −1 , 95% CI, R 2 = 0.66, p < 0.001) and” |
| Paper (a different field each row) | verbatim evidence | |
|---|---|---|
| • | The Weirdest People in the World? anthropology · 2010 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | CAN: Creative Adversarial Networks, Generating Art by Learning About Styles an art-history · 2017 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | Language Models are Few-Shot Learners artificial-intelligence · 2020 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | First M87 Event Horizon Telescope Results. I. The Shadow of the Supermassive B astronomy · 2019 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | Comparison of Three Nudge Interventions (Priming, Default Option, and Perceive behavioral-economics · 2017 | “The study had hoped to have a more representative sample, but it was not a requirement of the study’s design nor a theoretical basis for the” |
| • | Frequency and Repetition Effects in Lexical Memory cognitive-psychology · 1977 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change digital-humanities · 2016 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | The Psychological Consequences of Money economics · 2006 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| Paper (a different field each row) | verbatim evidence | |
|---|---|---|
| • | The Weirdest People in the World? anthropology · 2010 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | BERT: Pre-training of Deep Bidirectional Transformers for Language Understandi artificial-intelligence · 2018 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | Observational Evidence from Supernovae for an Accelerating Universe and a Cosm astronomy · 1998 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | Information Processing in Living Systems biology · 2016 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | SchNet: A Continuous-Filter Convolutional Neural Network for Modeling Quantum chemistry · 2017 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | Repetition Priming and Automaticity: Common Underlying Mechanisms? cognitive-psychology · 1990 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | Procedural priming effects on spontaneous inference formation consumer-psychology · 2004 | quotation withheld — the rights manifest does not clear this paper for redistribution |
| • | Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change digital-humanities · 2016 | quotation withheld — the rights manifest does not clear this paper for redistribution |
DATA/corpus.json by TOOLS/make_report_statistics.py. Built from corpus 1714 rec . 123,382 anchors . 2026-10-05 21:46:01 . ed3ab7. A surfaced cross-field link is a hypothesis to investigate, never a claimed discovery.