*United States v. Chester*, No. 13 CR 00774 (N.D. Ill. Oct. 7, 2016):

I suggested that the court missed (or summarily dismissed) the main point the President's Council of Science and Technology Advisers were making -- that there is an insufficient basis in the literature for concluding that "the error rate [is] roughly 2%," but the court's understanding of "the error rate" also merits comment. The description of the meaning of "the false positive rate" in note 3 (quoted above) is plainly wrong. Or, rather, it is subtly wrong. If the experts will testify that two bullets came from the same gun, they will be testifying that their tests were positive. If the tests are in error, the test results will be false positives. And if the false-positive error probability is only 2%, it sounds as if there is only a 2% probability "that [the] expert's testimony ... is in fact incorrect."As such, the report does not dispute the accuracy or acceptance of firearm toolmark analysis within the courts. Rather, the report laments the lack of scientifically rigorous “blackbox” studies needed to demonstrate the reproducibility of results, which is critical to cementing the accuracy of the method. Id. at 11. The report gives detailed explanations of how such studies should be conducted in the future, and the Court hopes researchers will in fact conduct such studies. See id. at 106. However, PCAST did find one scientific study that met its requirements (in addition to a number of other studies with less predictive power as a result of their designs). That study, the “Ames Laboratory study,” found that toolmark analysis has a false positive rate between 1 in 66 and 1 in 46. Id. at 110. The next most reliable study, the “Miami-Dade Study” found a false positive rate between 1 in 49 and 1 in 21. Thus, the defendants’ submission places the error rate at roughly 2%.^{3}The Court finds that this is a sufficiently low error rate to weigh in favor of allowing expert testimony. See Daubert v. Merrell Dow Pharms., 509 U.S. 579, 594 (1993) (“the court ordinarily should consider the known or potential rate of error”); United States v. Ashburn, 88 F. Supp. 3d 239, 246 (E.D.N.Y. 2015) (finding error rates between 0.9 and 1.5% to favor admission of expert testimony); United States v. Otero, 849 F. Supp. 2d 425, 434 (D.N.J. 2012) (error rate that “hovered around 1 to 2%” was “low” and supported admitting expert testimony). The other factors remain unchanged from this Court’s earlier ruling on toolmark analysis. See ECF No. 781.

3. Because the experts will testify as to the likelihood that rounds were fired from the same firearm, the relevant error rate in this case is the false positive rate (that is, the likelihood that an expert’s testimony that two bullets were fired by the same source is in fact incorrect).

But that is not how these probabilities work. The court's impression reflects what we can call a "false-positive fallacy." It is a variant on the well-known transposition fallacy (also loosely called the prosecutor's fallacy). Examiner-performance studies are incapable of producing what the court would like to know (and what it thought it was getting) -- "the likelihood that an expert’s testimony that two bullets were fired by the same source is in fact incorrect." The last phrase denotes the

*probability that a source hypothesis is false*. It can be called a source probability. The "false positive rate" is the

*probability that certain evidence will arise if the source hypothesis is true*. It can be called an evidence probability. As explained below, this evidence probability is but one of three probabilities that determine the source probability.

**I. Likelihoods: The Need to Consider Two Error Rates**

The so-called black-box studies can generate estimates of the evidence probabilities, but they cannot reveal the source probabilities. Think about how the performance study is designed. Examiners decide whether pairs of bullets or cartridges were discharged from the same source (

*S*) or from different guns (~

*S*). They are blinded to whether

*S*or ~

*S*is true, but the researchers control and know the true state of affairs (what forensic scientists like to call "ground truth"). The proportion of cases in which the examiners report a positive association (+

*E*) out of all the cases of

*S*can be written

*Prop*(+

*E*in cases of

*S*), or more compactly,

*Prop*(+

*E*|

*S*). This proportion leads to an estimate of the probability that, in practice, the examiners and others like them will report a positive association (+

*E*) when confronted with same-source bullets. This conditional probability for +

*E*given that

*S*is true can be abbreviated

*Prob*(+

*E*|

*S*). I won't be fastidious about the difference between a proportion and a probability and will just write

*P*(+

*E*|

*S*) for either, as the context dictates. In the long run, for the court's 2% figure (which is higher than the one observed false-positive proportion in the Ames study), we expect examiners to respond positively (+

*E*)

*when S is not true*(and they do reach a conclusion) only

*P*(+

*E*| ~

*S*) = 2% of the time.

Surprisingly, a small number like 2% for the "false-positive error rate"

*P*(+

*E*|

*~S*) does not necessarily mean that the positive finding +

*E*has

*any*probative value! Suppose that positive findings +

*E*occur just as often when

*S*is false as when

*S*is true. (Examiners who are averse to false-positive judgments might be prone to err on the side of false negatives.) If the false-negative error probability is

*P*(–

*E*|

*S*) = 98%, then examiners will tend to report –

*E*98% of the time for same-source bullets (

*S*), just as they report +

*E*98% of the time for different-source bullets (

*S*). Learning that such examiners found a positive association is of zero value in separating same-source cases from different-source cases. We may as well have flipped a coin. The outcome (the side of the coin, or the positive judgment of the examiner) bears no relationship to whether the

*S*is true or not.

Although a false negative probability of 98% is absurdly high, it illustrates the unavoidable fact that only when the ratio of the

*two likelihoods*,

*P*(+

*E*|

*S*) and

*P*(+

*E*| ~

*S*), exceeds 1 is a positive association positive evidence of a true association. Consequently, the court's thought that "

*the*relevant error rate in this case is the false positive rate" is potentially misleading. This likelihood is but one of the

*two*relevant likelihoods. (And there would be still more relevant likelihoods if there were more than two hypotheses to consider.)

**II. Prior Probabilities: The Need to Consider the Base Rate**

Furthermore, yet another quantity -- the mix of same-source and different-source pairs of bullets in the cases being examined -- is necessary to arrive at the court's understanding of "the false positive rate" as "the likelihood that an expert’s testimony that two bullets were fired by the same source is in fact incorrect."

__1__/ In technical jargon, the probability as described is the complement of the posterior probability (or positive predictive value in this context), and the posterior probability depends on not only on the two likelihoods, or evidence probabilities, but also on the "prior probability" for the hypotheses

*S*.

A few numerical examples illustrate the effect of the prior probability. Imagine that a performance study with 500 same-source pairs and 500 different-source pairs (that led to conclusions) found the outcomes given in Table 1.

Table 1. Outcomes of comparisons | |||

~S |
S |
||
---|---|---|---|

–E |
490 | 350 | 840 |

+E |
10 | 150 | 160 |

500 | 500 | ||

–E is a negative finding (the examiner decided there was no association).+ E is a positive finding (the examiner decided there was an association).S indicates that the cartridges came from bullets fired by the same gun.~ S indicates that the cartridges came from bullets fired by a different gun. |

The first column of the table states that in the different-source cases, examiners reported a positive association +

*E*in only 10 cases. Thus, their false-positive error rate was

*P*(+

*E*|

*~S*) = 10/500 = 2%. This is the figure used in

*Chester*. (The second column states that in the same-source cases, examiners reported a negative association 350 times. Thus, their false-negative rate was

*P*(–

*E*|

*S*) = 350/500 = 70%.)

But the bottom row of the table states that the examiners reported a positive association +

*E*for 10 different-source cases and 150 same-source cases. Of the 10 + 150 = 160 cases of positive evidence, 150 are correct, and 10 are incorrect. The rate of incorrect positive findings was therefore

*P*(~

*S*| +

*E*) = 10/160 = 6.25%. Within the four corners of the study, one might say, as the court did, that "the likelihood that an expert’s testimony that two bullets were fired by the same source is in fact incorrect" is only 2%. Yet, the rate of incorrect positive findings in the study exceeded 6%. The difference is not huge, but it illustrates the fact that the false-negative probability as well as the false-positive probability affects

*P*(~

*S*| +

*E*), which indicates how often an examiner who declares a positive association is wrong.

__2__/

Now let's change the mix of same- and different-source pairs of bullets from 50:50 to 10:90. We will keep the conditional-error probabilities the same, at

*P*(+

*E*|

*~S*) = 2% and

*P*(–

*E*|

*S*) = 70%. Table 2 meets these constraints:

Table 2. Outcomes of comparisons | |||

~S |
S |
||
---|---|---|---|

–E |
980 | 70 | 1050 |

+E |
20 | 30 | 50 |

1000 | 100 |

Row 2 shows that there are 20 false positives out of the 50 positively reported associations. The proportion of false positives in the modified study is

*P*(~

*S*| +

*E*) = 40%. But the false-positive rate

*P*(+

*E*| ~

*S*) is still 2% (20/1000).

**III. "When I'm 64": A Likelihood Ratio from the Ames Study**

The

*Chester*court may not have had a correct understanding of the 2% error rate it quoted, but the Ames study does establish that examiners are capable of distinguishing between same-source and different-source items on which they were tested. Their performance was far better than the outcomes in the hypothetical Tables 1 and 2. The Ames study found that across all the examiners studied,

*P*(+

*E*|

*S*) = 1075/1097 = 98.0%, and

*P*(+

*E*|~

*S*) = 22/1443 = 1.52% .

__3__/ In other words, on average, examiners made a correct positive associations 98.0/1.52 = 64 times more often when presented with same-source cartridges than they made incorrect positive associations when presented with different-source cartridges. This likelihood ratio, as it is called, means that when confronted with cases involving an even mix of same- and different-source items, over time and over all examiners, the pile of correct positive associations would be some 64 times higher than the pile of incorrect positive associations. Thus, in

*Chester*, Judge Tharp was correct in suggesting that the one study that satisfied PCAST's criteria offers an empirical demonstration of expertise at associating bullet cartridges with the gun that fired them.

Likewise, an examiner presenting a source attribution can point to a study deemed to be well designed by PCAST that found that a self-selected group of 218 examiners given cartridge cases from bullets fired by one type of handgun correctly identified more than 99 out of 100 same-gun cartridges and correctly excluded more than 98 out of 100 different-gun cartridges. For completeness, however, the examiner should add that he or she has no database with which to estimate the frequency of distinctive marks -- unless, of course, there is one that is applicable to the case at bar.

* * *

Whether the Ames study, together with other literature in the field, suffices to validate the expertise under

*Daubert*is a further question that I will not pursue here. My objective has been to clarify the meaning of and some of the limitations on the 2% false-positive error rate cited in

*Chester*. Courts concerned with the scientific validity of a forensic method of identification must attend to "error rates." In doing so, they need to appreciate that it takes two to tango. Both false-positive and false-negative conditional-error probabilities need to be small to validate the claim that examiners have the skill to distinguish accurately between positively and negatively associated items of evidence.

**Notes**

- Not wishing to be too harsh on the court, I might speculate that its thought that the only "relevant error rate" for positive associations is the false-positive rate might have been encouraged by the PCAST report's failure to present any data on negative error rates in its discussion of the performance of firearms examiners. A technical appendix to the report indicates that the related
likelihood is pertinent to the weight of the evidence, but this fact might be lost on the average reader -- even one who looks at the appendix.

- The PCAST report alluded to this effect in its appendix on statistics. That Judge Tharp did not pick up on this is hardly surprising.
- See David H. Kaye, PCAST and the Ames Bullet Cartridge Study: Will the Real Error Rates Please Stand Up?, Forensic Sci., Stat. & L., Nov. 1, 2016, http://for-sci-law.blogspot.com/2016/11/pcast-and-ames-study-will-real-error.html.

**More on the PCAST Report**

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