The Case for Technology Assisted Review and Statistical Sampling in Discovery

Technology Assisted Review (TAR) and Statistical Sampling can significantly reduce risk and improve productivity in eDiscovery processes.  TAR reduces risk by mathematically ranking documents in descending order of likely relevance. Productivity is increased by prioritizing review on the most relevant documents, while review of low ranked records may even be avoided entirely.    


Relevance ranking is an ordinal measure, meaning higher scores are more likely to be relevant, but a high score is not a guarantee that a document is relevant, and a low score is no guarantee that a document is not relevant.  Statistical sampling quantifies the degree that relevance ranking does not reflect true relevance.    With such measures, attorney stakeholders can make informed decisions about  the reliability and accuracy of the review process, thus quantifying actual risk of error and using that measurement to maximize the value of expensive manual review. Law firms that adopt these techniques are demonstrably faster, more informed  and productive than firms who rely solely on attorney reviewers who eschew TAR or statistical sampling.       



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