Choose a test

Choose a procedure from the scientific question and sampling design before looking at the observed results. Start by naming the population quantity in the null hypothesis, then determine the number of samples, whether observations are independent or paired, and whether each observation is scalar, multivariate, rectangular, or compositional.

Start with the null hypothesis

Null concerns	Data and design	API category
One or more scalar means	One, two, or several univariate samples	Univariate mean
One or more mean vectors	One, two, or several multivariate samples	Multivariate mean
One or more scalar variances	One, two, or several univariate samples	Variance
One or more covariance matrices	One, two, or several multivariate samples	Covariance
Mean and variance together	One or two univariate normal samples	Mean and variance
Mean vector and covariance together	One or two multivariate samples	Mean and covariance
An entire distribution	Two independent samples	Equality of distributions
Fit to a normal distribution	One univariate sample	Normality
Fit to a rectangular uniform distribution	One multivariate sample	Rectangular uniformity
Fit to uniformity on a probability simplex	One compositional sample	Simplex uniformity

A test of a mean, variance, or covariance answers a narrower question than a test of an entire distribution. Failure to reject one equality does not establish another. A joint test answers whether at least one component of its joint null fails; it does not identify which component changed.

Scalar parameters and goodness of fit

Question and design	Procedure choices	Main distinction
One scalar mean	ttest_1samp	Normal-theory one-sample inference
Two scalar means	ttest_2samp	Welch is the independent-sample default; pooled and paired modes encode different designs
Several scalar means	anova_oneway	Classical independent-groups ANOVA with a common variance
One scalar variance	chisquare_1samp	Normal-theory test against a specified variance
Two scalar variances	f_2samp	Normal-theory variance-ratio test
Several scalar variances or spreads	bartlett, levene, brown_forsythe	Bartlett is normal-theory; Levene and Brown–Forsythe use deviations from means and medians
One mean and variance jointly	as_1samp	Asymptotic likelihood-ratio test against specified normal parameters
Two means and variances jointly	pn_2samp, pl_2samp, muirhead_2samp, zxc_2samp, lrt_2samp	Respectively beta approximation, combined component tests, corrected approximation, exact calibration, and asymptotic LRT
Univariate normality	shapiro_wilk, shapiro_francia, jarque_bera, adjusted_jarque_bera, robust_jarque_bera	Shapiro tests use order statistics; moment tests target skewness and kurtosis and default to finite-sample Monte Carlo calibration

Choose a joint mean-and-variance test only when the scientific null really specifies both parameters. Do not use it as a substitute for inspecting which parameter matters. Likewise, select among the normality tests from the kinds of departures and sample-size regime relevant to the analysis, not by reporting the smallest of several p-values.

Multivariate means

Here n denotes sample size and p the number of features.

Design or regime	Procedure choices	Important condition
Classical one-sample mean vector	hotelling_1samp	Requires n > p and an invertible covariance estimate
Classical two-sample or paired mean vector	hotelling_2samp	Independent mode uses a common covariance; the fitted covariance must be invertible
Dense shift when covariance inversion is unsuitable	dempster_1samp, dempster_2samp, bs_1samp, bs_2samp	Use the paper-specific dimensional and covariance regimes
Coordinate-standardized dense shift	sd_1samp, sd_2samp	Requires usable marginal variance estimates
Unequal-covariance two-sample mean vector	yao_2samp, johansen_2samp, nvm_2samp, ky_2samp	Low-dimensional Behrens–Fisher approximations with method-specific degrees of freedom
Randomized high-dimensional comparison	ljw_2samp, thulin_2samp	Record projection or subspace controls and rng; Monte Carlo mode fixes auxiliary randomness across permutations
Coordinatewise Bayesian evidence	lyl_2samp	Returns maximum and component log Bayes factors, not a p-value
Several groups	schott_ksamp, zx_ksamp, cph_ksamp	Match common- versus unequal-covariance assumptions and the advertised dimensional regime

High-dimensional does not mean assumption-free. Trace, diagonal, random-projection, sparse, and factor-model tests target different alternatives and require different spectral or moment conditions. Use the method-specific validation ledger before selecting one from sample size and dimension alone.

Covariance and joint multivariate parameters

Question and regime	Procedure choices	Main distinction
One covariance against a specified matrix	wl_1samp	Random-projection method; record rng and verify the advertised Gaussian regime
Two high-dimensional covariances	lc_2samp, clx_2samp, wl_2samp	Li–Chen targets a global Frobenius departure, CLX a maximum standardized entry, and Wu–Li projected variance ratios
Two covariances, Bayesian evidence	lyl_2samp	Uses the paper’s known-zero-mean Gaussian model and returns conditional-regression log Bayes-factor evidence without a universal threshold
Several covariance matrices	schott_2001_ksamp, schott_2007_ksamp	The 2001 procedure uses a pooled inverse; the 2007 procedure targets a high-dimensional regime
One mean vector and covariance against specified values	llzs_1samp, lrt_1samp	LLZS is high-dimensional; the LRT is fixed-dimensional and needs an invertible fitted covariance
Two mean vectors and covariances jointly	hn_2samp	High-dimensional joint test under its source paper’s moment and trace conditions

The covariance CLX procedure is public, but Fisher’s covariance procedure and SHT’s distinct Cai–Liu–Xia mean test is validation-blocked and therefore does not appear in the selection table. The two public lyl_2samp functions are likewise distinct procedures; module qualification is part of each public name. In particular, the covariance version does not estimate or remove a mean: center observations externally only when a scientific design justifies treating the resulting values as observations from the paper’s fixed zero-mean model.

Distributional and structured-domain questions

Null hypothesis	Procedure	Design boundary
Two independent samples have the same distribution	equaldist.bg_2samp	Pooled observations must be exchangeable; use exact enumeration when feasible or corrected Monte Carlo calibration
Observations are uniform on declared rectangular bounds	uniformity.ym_interpoint	Monte Carlo is the default calibration for q1, q2, and q3; boundary points are allowed
Observations are uniform on declared rectangular bounds	uniformity.ym_quantile	Every coordinate must be strictly inside its bounds before the normal-quantile transform
Compositions are uniform on a probability simplex	simplex.uniformity	Rows must lie in the strict simplex interior; choose a symmetric or general Dirichlet alternative

The bounds in a rectangular-uniformity test and the simplex itself are part of the null model. Estimating support limits from the same data changes that model and is not handled automatically.

A practical decision sequence

1. Write the null and alternative in words. Decide whether the target is a mean, dispersion parameter, joint model, full distribution, or domain- specific uniform law.

2. Identify sampling units and dependence. Matched observations require an explicit paired procedure. Unrestricted tests here do not model clusters, repeated measures, or time dependence.

3. Determine the dimensional regime. Check n, p, covariance rank, group balance, and whether the reference is fixed-dimensional, high-dimensional, exact, asymptotic, or simulation based.

4. Match the alternative. Global squared-distance, maximum-coordinate, randomized-projection, and Bayes-factor methods can have very different power against the same broad null.

5. Choose calibration and random controls in advance. When using Monte Carlo calibration, set n_resamples from the required precision and pass a reproducible rng when exact replay matters.

6. Read the result according to its contract. A p-value, a Monte Carlo estimate with uncertainty, and a log Bayes factor are not interchangeable.

Call the selected procedure

Functions return a result object; they do not print or make a reject/retain decision automatically. Supply design choices explicitly and retain the result for both reporting and programmatic use:

from pysht.mean import ttest_2samp

result = ttest_2samp(
    treatment,
    control,
    alternative="two-sided",
    equal_var=False,
)

print(result)

This requests Welch’s independent-samples t test. Setting equal_var=True changes the statistical model; setting paired=True changes the sampling design. Arguments should follow the study design rather than the observed data. Consult the category page in the API reference for exact signatures and defaults.

Scope boundaries

The SHT compatibility catalog does not provide regression, generalized linear models, survival methods, repeated-measures models, survey weights, arbitrary missing-data handling, user-defined restricted permutations, or automatic multiple-testing adjustments. Use a library designed for those models rather than substituting a superficially similar standalone test.

Before analysis, review the data and assumptions guide. After computing a test, use the result-object guide to interpret what was returned.