Statistical Sampling

Properly designed and implemented, statistical sampling is a valuable tool in a litigation setting. We are often involved in the design and implementation of sampling strategies, including the critical evaluation of opposing parties’ samples. When it is impractical or expensive to evaluate every observation in a population, a properly selected sample can yield robust inferences about the characteristics of the underlying population. 

We have applied sampling methods in cases involving data spoliation, fraud, product defects, financial instruments, medical claims, intellectual property, consumer products, real estate, and employment. For example, we have conducted statistical sampling projects to evaluate the quality and reliability of electronic data compiled from source documents, select records for more detailed investigation, and extrapolate results from records with adequate information to those missing data.

Statistical sampling is useful when sufficient data are not available electronically, the cost of collecting and analyzing every individual observation from a population is too high or time consuming, or a population is too large to reasonably analyze each observation. We have also applied statistical analysis to questions relating to statistical power and determination of sample sizes. Appropriate sample sizes are contingent, for example, on the variable of interest. Samples that are appropriately powered for one variable may not contain enough observations to draw reliable inferences on other variables of interest.

Key Questions in Sampling

Many of the challenges in statistical analysis are associated with the selection of a representative sample and the use of that sample to estimate characteristics of the population from which the sample was drawn. To properly select a sample or evaluate the quality of an existing sample, our analyses often focus on key questions about sample design and implementation, including:

  • What is the question to be answered?
  • What is the population of interest?
  • What level of accuracy is required?
  • What size sample is required to achieve the desired accuracy level?
  • Is there a benefit to selecting a stratified sample?
  • Is the sample representative? Is it biased?
  • Were the sampling plan and data collection properly implemented?
  • Is the selected sample sufficient to answer the question of interest?
  • Which method should be used to extrapolate from the sample to the population of interest?
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