Hypothesis testing

test_statistics.qmu(mu, muhat, max_logpdf, ...)	Test statistic \(q_{\mu}\), see eq.
test_statistics.qmu_tilde(mu, muhat, ...)	Alternative test statistics, \(\tilde{q}_{\mu}\), see eq.
test_statistics.q0(mu, muhat, max_logpdf, logpdf)	Discovery test statistics, \(q_{0}\) see eq.
test_statistics.get_test_statistic(test_stat)	Retreive the test statistic function
test_statistics.compute_teststatistics(mu, ...)	Compute test statistics
upper_limits.ComputerWrapper(computer)	Wrapper for the computer function to track inputs and outputs
upper_limits.find_poi_upper_limit(...[, ...])	Find upper limit for parameter of interest, \(\mu\)
upper_limits.find_root_limits(computer[, ...])	Find upper and lower bracket limits for the root finding algorithm
utils.pvalues(delta_test_statistic, ...)	Calculate the p-values for the observed test statistic under the signal + background and background-only model hypotheses.
utils.expected_pvalues(...)	Calculate the \(p\) values corresponding to the median significance of variations of the signal strength from the background only hypothesis \(\mu=0\) at \((-2,-1,0,1,2)\sigma\).
distributions.AsymptoticTestStatisticsDistribution(shift)	The distribution the test statistic in the asymptotic case.
distributions.EmpricTestStatisticsDistribution(samples)	Create emprical distribution.
asymptotic_calculator.compute_asymptotic_confidence_level(...)	Compute p values i.e. \(p_{s+b}\), \(p_b\) and \(p_s\).
toy_calculator.compute_toy_confidence_level(...)	Compute confidence limits i.e. \(CL_{s+b}\), \(CL_b\) and \(CL_s\).