Global News
A new report from SettlementAnalytics™ provides legal professionals with an introduction to appropriate analytical methods and online tools to facilitate a better understanding of ‘Big Legal Data’ and how it should be used. The report introduces key concepts at an introductory level and refers to applications which illustrate how to evaluate the statistical merit of legal data.
The central problem is that legal professionals do not analyze quantitative data merely to observe historical data facts, but rather in an effort to draw a meaningful inference about the present, and to make decisions. Although it is widely understood, it is sometimes forgotten that past data is not a reliable metric for present insight — the past is only a sample of what could happen and often it is a very imperfect one.
The bottom line is that not all samples of legal data contain sufficient information to be usefully applied to a decision. By the time big data sets are filtered down to the type of matter that is relevant, sample sizes may be too small and measurements may be exposed to potentially large sampling errors. If Big Data becomes ‘small data', it could be quite useless or worse.
"To be of value in real world decisions, legal data analytics must be able to distinguish between the inherent randomness in historical data samples and statistically meaningful legal track records," said Robert Parnell, author of the report and founder of SettlementAnalytics. "This is the stuff of inferential statistics."
The report provides a mostly non-technical introduction to the subject, which should be accessible to legal professionals without a quantitative background. Example analyses are included, which show how to quantify uncertainty in the measurement of judicial decisions, and how to determine if a law firm’s track record is statistically significant relative to its peer group. The results of statistical analyses are presented in charts and tables.
The report graphically illustrates an important point: although the volume of data available to legal professionals will sometimes be sufficient to distinguish an informational signal from the random noise in data, this will not always be the case. Using the methods outlined in this report, litigants and counsel will be able to interrogate the statistical validity of court data and evaluate the significance of quantitative legal metrics.
Data analytics is more complex than is generally acknowledged. Although it is appealing to view data analysis as a simple tool, there is a danger of neglecting the science in what is basically data science. The consequences of this can be to undermine sound decision making. To draw an analogy, legal data analytics without inferential statistics is like legal argument without case law or rules of precedent — it lacks a meaningful point of reference and authority.
Where data analytics is used for decision making, it must include an allowance for the role of inferential statistics – only then can its informational content be properly evaluated. By using inferential statistical methods and paying careful attention to the complexities of data analysis, corporate litigants and Big Law can benefit from this new frontier in Big Data.
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