Glossary
Cross-domain terminology mapping
NextStat originates from particle physics (HEP) but serves data scientists, quants, and biologists. This glossary maps core concepts across all four domains so you can read the documentation in your own language.
How to use this page
Find your domain in the column headers. The NextStat API column shows the function or concept you'll use regardless of which field you come from.
Core Inference Concepts
| HEP / Physics | Data Science | Quant / Finance | Bio / Pharma | NextStat API |
|---|---|---|---|---|
| Nuisance parameter | Latent variable / systematic | Hidden factor / risk driver | Confounding variable | model.parameters() |
| Parameter of interest (μ) | Target parameter / scale factor | Coefficient of interest | Treatment effect size | poi="mu" |
| Profile likelihood | Loss function (marginalised) | Concentrated log-likelihood | Restricted likelihood | profiled_q0_loss() |
| Maximum likelihood estimate | .fit() / best params | MLE / point estimate | Point estimate | nextstat.fit(model) |
| Profile likelihood interval | Confidence interval | Confidence interval | Confidence interval | nextstat.scan() |
| Significance (Z₀) | Test statistic / p-value | t-statistic / p-value | Statistical significance | nextstat.significance() |
| CLs exclusion limit | Upper bound on effect size | Confidence bound (one-sided) | Non-inferiority bound | nextstat.upper_limit() |
| Toy Monte Carlo | Bootstrap / MC simulation | Monte Carlo simulation | Simulation study | nextstat.sample() |
Model Types
| HEP / Physics | Data Science | Quant / Finance | Bio / Pharma | NextStat API |
|---|---|---|---|---|
| HistFactory workspace | Model config / experiment spec | — | — | nextstat.from_pyhf() |
| — | Linear / logistic regression | OLS / logit | GLM (treatment model) | nextstat.ols() / logistic() |
| — | Random effects model | Panel FE / RE | Mixed-effects model | nextstat.panel_fe() |
| — | State-space model | Kalman filter | Hidden Markov (Gaussian) | nextstat.kalman_filter() |
| — | — | DiD / event study | Pre-post intervention | nextstat.did() |
| — | — | IV / 2SLS | Instrumental variable | nextstat.iv_2sls() |
| — | Causal inference (ATE) | AIPW / doubly-robust | Treatment effect (AIPW) | nextstat.aipw() |
| — | — | — | Cox PH / Weibull survival | nextstat.CoxPhModel() |
| — | — | — | Population PK (NLME) | nextstat.nlme() |
Data & Infrastructure
| HEP / Physics | Data Science | Quant / Finance | Bio / Pharma | NextStat API |
|---|---|---|---|---|
| Histogram template | Binned distribution | Histogram / density estimate | Frequency distribution | SoftHistogram() |
| Ranking plot | Feature importance | Sensitivity analysis | Covariate impact | nextstat.ranking() |
| Asimov dataset | Representative synthetic data | Expected-value dataset | Null simulation | nextstat.asimov_data() |
| ROOT file / TTree | Parquet / Arrow Table | DataFrame (Polars) | CSV / SAS dataset | nextstat.from_arrow() |
| Validation report | Model card / audit log | Model risk artifact (SR 11-7) | IQ/OQ/PQ pack (GxP) | nextstat validation-report |
| CUDA / Metal GPU | GPU training (PyTorch) | GPU acceleration | — | device="cuda" / "metal" |