In some wage and hour analyses a statistical random sample is needed to help address liability and damage issues. A sample may be required in employer’s self audit, regulatory investigation, or lawsuit involving FLSA, overtime, and wage and hour issues, such as unpaid meal periods..In some instances, a weighted sampling routine may be appropriate. For instance, in this example.we are going to select a random sample of 100 employees for an employer’s self audit of its wage and hour practices. Time and payroll data for the sample of employees will be assembled by the employer for the selected individuals.
The sample contains four different types of employees that work at the company. The goal is to have the employee sample be representative of the overall universe of employees at the company.
Roughly half of the employees in the sample are type I employees, 25% are type II, and 20% are type III employees. 5% are type IV employees. The employer maintains the data for each type of employee in separate modules of its database and must access each type of employee separately
In this instance, some type of weighted sampling routine would be appropriate. .For instance, the sample could be selected by first randomizing the employees of each type. Then a weighted sample based on the proportion of each type of employee at the company can be selected. For instance, 50 random employees of type I, 25 random employees of type II, 20 random employees of type III, and 5 random employees of type IV.
In business and commercial litigation, it is frequently alleged that the offending party’s actions resulted in a lost of business profit for the other party. In some instances, the issue is that the offending party’s actions prevented the pursuit of a given business opportunity as opposed to the reduction of profits or revenue from existing business. In other words, because of the offending party’s actions the business revenue and associated profit, simply did not happen.