Case Study

How do you recover stolen cash when you don’t know how much was stolen? A&I uses available and relevant data to provide an estimate supported by federal District and Appeals courts.

When a client faces a class action suit, a statistician creates a protocol, establishes key criteria and uses sampling to determine how many plaintiffs actually represent the class.

A&I trains a statistical algorithm to correctly identify customer payments lacking personal or account data as payments for services rendered.

A&I looks beyond engineering reports of potential nuclear power plant accidents to deliver a “market-based view” of the risk as revealed by insurance premiums plant operators pay.

Identifying a critical statistical bias in the way the Department of Health & Human Services unfairly penalizes states for errors in determining Medicaid eligibility and payments.

A principal of A&I replaces an incorrectly applied model with a proper statistical test to expose flaws in statistical proof of discrimination.

A&I’s analysis of an auditor’s estimate of alleged Medicare overcharging reveals bias in the form of faulty sampling and an inaccurate formula for calculating overcharges.

A&I’s review of an engineer’s report reveals a failure to randomly sample damages; a new statistical model accurately predicts a much lower rate of damages.

Faced with a biased environmental test procedure, quantitative analysis and industry experience determine if a client should invest in additional testing to earn a designation as clean fill.

Dr. William Huber of A&I led a team of statisticians, economists, and database engineers in modeling, analyzing, predicting, and mapping availability of high-speed broadband services throughout the United States.

Statistical errors identified by Dr. William Huber of A&I led to a summary judgment on behalf of a defendant in natural resources damages litigation.