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**Jurimetrics** is the application of quantitative methods, and often especially probability and statistics, to law.^{[1]} In the United States, the journal *Jurimetrics* is published by the American Bar Association and Arizona State University.^{[2]}

The term was coined in 1949 by Lee Loevinger in his article "Jurimetrics: The Next Step Forward".^{[1]}^{[3]} Showing the influence of Oliver Wendell Holmes, Jr., Loevinger quoted^{[4]} Holmes' celebrated phrase that:

**“For the rational study of the law the blackletter man may be the man of the present, but the man of the future is the man of statistics and the master of economics.”**^{[5]}

The first work on this topic is attributed to Nicolaus I Bernoulli in his doctoral dissertation *De Usu Artis Conjectandi in Jure*, written in 1709.

- Airline deregulation
^{[6]} - Analysis of police stops
^{[7]}(Negative binomial regression) - Challenging election results
^{[8]}(Hypergeometric distribution) - False conviction rate of inmates sentenced to death
^{[9]} - Legal evidence
^{[10]}^{[11]}^{[12]}(Bayesian network) - Legal informatics
- Predictive policing
^{[13]} - Predictors of criminal recidivism
^{[14]} - Prosecutor's fallacy

In 2018, California's legislature passed Senate Bill 826, which requires all publicly held corporations based in the state to have a minimum number of women on their board of directors.^{[15]}^{[16]} Boards with five or fewer members must have at least two women, while boards with six or more members must have at least three women.

Using the binomial distribution, we may compute what the probability is of violating the rule laid out in Senate Bill 826 by the number of board members. The probability mass function for the binomial distribution is:

where is the probability of getting successes in trials, and is the binomial coefficient. For this computation, is the probability that a person qualified for board service is female, is the number of female board members, and is the number of board seats. We will assume that .

Depending on the number of board members, we are trying compute the cumulative distribution function:

With these formulas, we are able to compute the probability of violating Senate Bill 826 by chance:

3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
---|---|---|---|---|---|---|---|---|---|

0.50 | 0.31 | 0.19 | 0.34 | 0.23 | 0.14 | 0.09 | 0.05 | 0.03 | 0.02 |

As Ilya Somin points out^{[15]}, a significant percentage of firms - without any history of sex discrimination - could be in violation of the law.

In more male-dominated industries, such as technology, there could be an even greater imbalance. Suppose that instead of parity in general, the probability that a person who is qualified for board service is female is 40%; this is likely to be a high estimate, given the predominance of males in the technology industry. Then the probability of violating Senate Bill 826 by chance may be recomputed as:

3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
---|---|---|---|---|---|---|---|---|---|

0.65 | 0.48 | 0.34 | 0.54 | 0.42 | 0.32 | 0.23 | 0.17 | 0.12 | 0.08 |

Bayes' theorem states that, for events and , the conditional probability of occurring, given that has occurred, is:

Using the law of total probability, we may expand the denominator as:

Then Bayes' theorem may be rewritten as:

This may be simplified further by defining the prior odds of event occurring and the likelihood ratio as:

Then the compact form of Bayes' theorem is:

Different values of the posterior probability, based on the prior odds and likelihood ratio, are computed in the following table:

Likelihood Ratio | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|

Prior Odds | 1 | 2 | 3 | 4 | 5 | 10 | 15 | 20 | 25 | 50 |

0.01 | 0.01 | 0.02 | 0.03 | 0.04 | 0.05 | 0.09 | 0.13 | 0.17 | 0.20 | 0.33 |

0.02 | 0.02 | 0.04 | 0.06 | 0.07 | 0.09 | 0.17 | 0.23 | 0.29 | 0.33 | 0.50 |

0.03 | 0.03 | 0.06 | 0.08 | 0.11 | 0.13 | 0.23 | 0.31 | 0.38 | 0.43 | 0.60 |

0.04 | 0.04 | 0.07 | 0.11 | 0.14 | 0.17 | 0.29 | 0.38 | 0.44 | 0.50 | 0.67 |

0.05 | 0.05 | 0.09 | 0.13 | 0.17 | 0.20 | 0.33 | 0.43 | 0.50 | 0.56 | 0.71 |

0.10 | 0.09 | 0.17 | 0.23 | 0.29 | 0.33 | 0.50 | 0.60 | 0.67 | 0.71 | 0.83 |

0.15 | 0.13 | 0.23 | 0.31 | 0.38 | 0.43 | 0.60 | 0.69 | 0.75 | 0.79 | 0.88 |

0.20 | 0.17 | 0.29 | 0.38 | 0.44 | 0.50 | 0.67 | 0.75 | 0.80 | 0.83 | 0.91 |

0.25 | 0.20 | 0.33 | 0.43 | 0.50 | 0.56 | 0.71 | 0.79 | 0.83 | 0.86 | 0.93 |

0.30 | 0.23 | 0.38 | 0.47 | 0.55 | 0.60 | 0.75 | 0.82 | 0.86 | 0.88 | 0.94 |

If we take to be some criminal behavior and a criminal complaint or accusation, Bayes' theorem allows us to determine the conditional probability of a crime being committed. More sophisticated analyses of evidence can be undertake with the use of Bayesian networks.

In recent years, there has been a growing interest in the use of screening tests to identify drug users on welfare, potential mass shooters^{[17]}, and terrorists.^{[18]} The efficacy of screening tests can be analyzed using Bayes' theorem.

Suppose that there is some binary screening procedure for an action that identifies a person as testing positive or negative for the action. Bayes' theorem tells us that the conditional probability of taking action , given a positive test result, is:

For any screening test, we must be cognizant of its sensitivity and specificity. The screening test has sensitivity and specificity . The sensitivity and specificity can be analyzed using concepts from the standard theory of statistical hypothesis testing:

- Sensitivity is equal to the statistical power , where is the type II error rate
- Specificity is equal to , where is the type I error rate

Therefore, the form of Bayes' theorem that is pertinent to us is:

Suppose that we have developed a test with sensitivity and specificity of 99%, which is likely to be higher than most real-world tests. We can examine several scenarios to see how well this hypothetical test works:

- We screen welfare recipients for cocaine use. The base rate in the population is approximately 1.5%
^{[19]}, assuming no differences in use between welfare recipients and the general population. - We screen men for the possibility of committing mass shootings or terrorist attacks. The base rate is assumed to be 0.01%.

With these base rates and the hypothetical values of sensitivity and specificity, we may calculate the posterior probability that a positive result indicates the individual will actually engage in each of the actions:

Drug Use | Mass Shooting |
---|---|

0.6012 | 0.0098 |

Even with very high sensitivity and specificity, the screening tests only return posterior probabilities of 60.1% and 0.98% respectively for each action. Under more realistic circumstances, it is likely that screening would prove even less useful than under these hypothetical conditions. The problem with any screening procedure for rare events is that it is very likely to be too imprecise, which will identify too many people of being at risk of engaging in some undesirable action.

The difference between jurimetrics and law and economics is that jurimetrics investigates legal questions from a probabilistic/statistical point of view, while law and economics addresses legal questions using standard microeconomic analysis. A synthesis of these fields is possible through the use of econometrics (statistics for economic analysis) and other quantitative methods to answer relevant legal matters.

- Bayesian inference
- Disparate impact#Statistical criticism of disparate impact
- Forensic statistics
- Law and economics
- Rules of evidence for expert testimony
- Simpson's paradox#UC Berkeley gender bias
- Survival analysis

- ^
^{a}^{b}Garner, Bryan A. (2001). "jurimetrics".*A Dictionary of Modern Legal Usage*. p. 488. ISBN 0195142365. **^**"Jurimetrics". American Bar Association. Retrieved 2015-02-06.**^**Loevinger, Lee (1949). "Jurimetrics--The Next Step Forward".*Minnesota Law Review*.**33**: 455.**^**Loevinger, L. "Jurimetrics: Science and prediction in the field of law".*Minnesota Law Review*, vol. 46, HeinOnline, 1961.**^**Holmes,*The Path of the Law*, 10*Harvard Law Review*(1897) 457.**^**Moore, Thomas Gale (1986). "U. S. Airline Deregulation: Its Effects on Passengers, Capital, and Labor".*The Journal of Law & Economics*.**29**(1): 1–28. doi:10.1086/467107. ISSN 0022-2186. JSTOR 725400.**^**Gelman, Andrew; Fagan, Jeffrey; Kiss, Alex (2007). "An Analysis of the New York City Police Department's "Stop-and-Frisk" Policy in the Context of Claims of Racial Bias".*Journal of the American Statistical Association*.**102**(479): 813–823. doi:10.1198/016214506000001040. ISSN 0162-1459. JSTOR 27639927.**^**Finkelstein, Michael O.; Robbins, Herbert E. (1973). "Mathematical Probability in Election Challenges".*Columbia Law Review*.**73**(2): 241. doi:10.2307/1121228. JSTOR 1121228.**^**Kennedy, Edward H.; Hu, Chen; O’Brien, Barbara; Gross, Samuel R. (2014-05-20). "Rate of false conviction of criminal defendants who are sentenced to death".*Proceedings of the National Academy of Sciences*.**111**(20): 7230–7235. Bibcode:2014PNAS..111.7230G. doi:10.1073/pnas.1306417111. ISSN 0027-8424. PMC 4034186. PMID 24778209.**^**Fenton, Norman; Neil, Martin; Lagnado, David A. (2013). "A General Structure for Legal Arguments About Evidence Using Bayesian Networks".*Cognitive Science*.**37**(1): 61–102. doi:10.1111/cogs.12004. ISSN 1551-6709. PMID 23110576.**^**Vlek, Charlotte S.; Prakken, Henry; Renooij, Silja; Verheij, Bart (2014-12-01). "Building Bayesian networks for legal evidence with narratives: a case study evaluation".*Artificial Intelligence and Law*.**22**(4): 375–421. doi:10.1007/s10506-014-9161-7. ISSN 1572-8382.**^**Kwan, Michael; Chow, Kam-Pui; Law, Frank; Lai, Pierre (2008). Ray, Indrajit; Shenoi, Sujeet (eds.). "Reasoning About Evidence Using Bayesian Networks".*Advances in Digital Forensics IV*. IFIP — The International Federation for Information Processing. Springer US.**285**: 275–289. doi:10.1007/978-0-387-84927-0_22. ISBN 9780387849270.**^**Perry, Walter L.; McInnis, Brian; Price, Carter C.; Smith, Susan; Hollywood, John S. (2013). "Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations".*RAND Corporation*. Retrieved 2019-08-16.**^**Spivak, Andrew L.; Damphousse, Kelly R. (2006). "Who Returns to Prison? A Survival Analysis of Recidivism among Adult Offenders Released in Oklahoma, 1985 – 2004".*Justice Research and Policy*.**8**(2): 57–88. doi:10.3818/jrp.8.2.2006.57. ISSN 1525-1071.- ^
^{a}^{b}Somin, Ilya (2018-10-04). "California's Unconstitutional Gender Quotas for Corporate Boards".*Reason.com*. The Volokh Conspiracy. Retrieved 2019-08-13. **^**Stewart, Emily (2018-10-03). "California just passed a law requiring more women on boards. It matters, even if it fails".*Vox*. Retrieved 2019-08-13.**^**Gillespie, Nick (2018-02-14). "Yes, This Is a Good Time To Talk About Gun Violence and How To Reduce It".*Reason.com*. Retrieved 2019-08-17.**^**"Terrorist Screening Center".*Federal Bureau of Investigation*. Retrieved 2019-08-17.**^**"What is the scope of cocaine use in the United States?".*National Institute on Drug Abuse*. Retrieved 2019-08-17.

- Finkelstein, Michael. O; Levin, Bruce (2015).
*Statistics for Lawyers*. Statistics for Social and Behavioral Sciences (3rd ed.). New York, NY: Springer. ISBN 9781441959843.

- Bernoulli (1709). The use of the Art of conjecturing in Law.
- Kadane, J.B. (2006). Misuse of Bayesian Statistics in Court, CHANCE, 19, 2, 38-40.
- Stern & Kadane (2014). Compensating for the loss of a chance. Department of Statistics, Carnegie Mellon University.
*Jurimetrics, The Journal of Law, Science, and Technology*