For other examples, see David H. Kaye et al. In Vuyanich v. Republic National Bank , F. It is doubtful, however, that economic knowledge sheds much light on the assumption, and it would have been simple to perform a less restrictive analysis.
See The Evolving Role of Statistical Assessments as Evidence in the Courts, supra note 1, at recommending that the expert be free to consult with colleagues who have not been retained. Statisticians analyze data using a variety of methods. There is much to be said for looking at the data in several ways.
To permit a fair evaluation of the analysis that is eventually settled on, however, the testifying expert can be asked to explain how that approach was developed. The collection of data often is expensive and subject to errors and omissions. Moreover, careful exploration of the data can be time-consuming. To minimize debates at trial over the accuracy of data and the choice of analytical techniques, pretrial discovery procedures should be used, particularly with respect to the quality of the data and the method of analysis. Also, flaws in the data can undermine any statistical analysis, and data quality is often determined by study design.
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In many cases, statistical studies are used to show causation. Do food additives cause cancer? Does capital punishment deter crime? Would additional disclosures. William W. See The Special Comm. F; see also David H.
For introductory treatments of data collection, see, for example, David Freedman et al. Notz, Statistics: Concepts and Controversies 6th ed. The design of studies to investigate causation is the first topic of this section. Sample data can be used to describe a population.
The population is the whole class of units that are of interest; the sample is the set of units chosen for detailed study. Inferences from the part to the whole are justified when the sample is representative.
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Sampling is the second topic of this section. Finally, the accuracy of the data will be considered. Because making and recording measurements is an error-prone activity, error rates should be assessed and the likely impact of errors considered. Data quality is the third topic of this section. When causation is the issue, anecdotal evidence can be brought to bear.
So can observational studies or controlled experiments. Anecdotal reports may be of value, but they are ordinarily more helpful in generating lines of inquiry than in proving causation. See also Michael D. Green et al. In medicine, evidence from clinical practice can be the starting point for discovery of cause-and-effect relationships. For examples, see David A. Box-Steffensmeier et al. Anecdotal evidence is rarely definitive, and some courts have suggested that attempts to infer causation from anecdotal reports are inadmissible as unsound methodology under Daubert v.
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Drawing such a conclusion from temporal relationships leads to the blunder of the post hoc ergo propter hoc fallacy. Chevron USA Inc. Matrixx Initiatives, Inc. Siracusano, S. Randomized controlled experiments are ideally suited for demonstrating causation. Anecdotal evidence usually amounts to reports that events of one kind are followed by events of another kind. Typically, the reports are not even sufficient to show association, because there is no comparison group.
For example, some children who live near power lines develop leukemia. Does exposure to electrical and magnetic fields cause this disease? The anecdotal evidence is not compelling because leukemia also occurs among children without exposure. If exposure causes the disease, the rate should be higher among the exposed and lower among the unexposed.
That would be association. The next issue is crucial: Exposed and unexposed people may differ in ways other than the exposure they have experienced.
For example, children who live near power lines could come from poorer families and be more at risk from other environmental hazards. Such differences can create the appearance of a cause-and-effect relationship. Other differences can mask a real relationship. Cause-and-effect relationships often are quite subtle, and carefully designed studies are needed to draw valid conclusions. An epidemiological classic makes the point. At one time, it was thought that lung cancer was caused by fumes from tarring the roads, because many lung cancer patients lived near roads that recently had been tarred.
This is anecdotal evidence. But the argument is incomplete. For one thing, most people—whether exposed to asphalt fumes or unexposed—did not develop lung cancer. A comparison of rates was needed. The epidemiologists found that exposed persons and unexposed persons suffered from lung cancer at similar rates: Tar was probably not the causal agent.
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Exposure to cigarette smoke, however, turned out to be strongly associated with lung cancer. This study, in combination with later ones, made a compelling case that smoking cigarettes is the main cause of lung cancer. A good study design compares outcomes for subjects who are exposed to some factor the treatment group with outcomes for other subjects who are. There are problems in measuring exposure to electromagnetic fields, and results are inconsistent from one study to another. For such reasons, the epidemiological evidence for an effect on health is inconclusive.
Linet et al. This was a matched case-control study. Cohort studies soon followed. See Green et al. Now there is another important distinction to be made—that between controlled experiments and observational studies. In a controlled experiment, the investigators decide which subjects will be exposed and which subjects will go into the control group. In observational studies, by contrast, the subjects themselves choose their exposures. Because of self-selection, the treatment and control groups are likely to differ with respect to influential factors other than the one of primary interest.
These other factors are called lurking variables or confounding variables. Many confounders have been proposed to explain the association between smoking and lung cancer, but careful epidemiological studies have ruled them out, one after the other. Confounding remains a problem to reckon with, even for the best observational research. For example, women with herpes are more likely to develop cervical cancer than other women.
Some investigators concluded that herpes caused cancer: In other words, they thought the association was causal. Later research showed that the primary cause of cervical cancer was human papilloma virus HPV.