Statistical significance testing is a fundamental principle relevant to mass tort cases involving complex scientific information (e.g., pharmaceutical and medical device cases as well as toxic torts and consumer fraud cases). Simply stated, if a scientist wants to show that one factor causes changes in another factor (e.g., a drug treatment, a chemical exposure, a source of radiation), conventionally he must rely upon statistical significance testing to demonstrate that difference.
Statistical significance testing is a fundamental principle relevant to mass tort cases involving complex scientific information (e.g., pharmaceutical and medical device cases as well as toxic torts and consumer fraud cases). Simply stated, if a scientist wants to show that one factor causes changes in another factor (e.g., a drug treatment, a chemical exposure, a source of radiation), conventionally he must rely upon statistical significance testing to demonstrate that difference. Demonstrating that a factor does not cause changes in another factor is a more nuanced and complex issue, but statistical significance testing also becomes a critical factor.
As we have discussed before on this blog, statistical significance testing is necessary because, before concluding that the findings of a study are reliable, we must rule out the role of chance (in addition to addressing confounding and bias). In other words, if we want to rely upon a study showing that an exposure is linked to changes in disease rates, it must be shown that the changes demonstrated in that study are not due to random chance.
Yet the need for significance testing in science is so fundamental that it is difficult to find specific quotes that provide the rules for when and how statistical significance testing must relied upon. For example, when can we rely upon a relaxed p value of 0.1 as opposed to the more conventional 0.05?
There have been robust debates in the scientific literature – as well as in the courtroom and in the blogosphere – as to when and how one must rely upon statistical significance testing to prove (or disprove) a scientific finding. As an example of this, I urge everyone to take a look at this post by Nathan Schachtman on Deborah Mayo’s blog.
I describe below why this post should be of interest to you and some take-home messages that you may find of value in your practices.
A Brief History of the Post
First, a brief history of the post and why it should be of interest to lawyers trying cases involving complex scientific issues:
Why You Should Read the Post
It is worth going through the NY Times OpEd, Schathman’s scholarly and provocative post, and all the comments. I attempt to simplify the discussion and extract some of the more interesting and relevant examples below:
“Please Nathan, the next time you or your colleagues cross-examine me, how about asking up front whether I actually think the treatment at issue is harmful based on the evidence as I know it. I routinely forewarn those who ask me to testify that they may not like the answers I give. Interestingly, this has only seemed to turn away defense lawyers, not plaintiff lawyers.”
Essentially, Dr. Greenland (an accomplished and scholarly epidemiologist) is simply saying that he is willing to testify for the plaintiffs on statistical methodological principles, even when he believes there is no reliable evidence supporting their cases. He counsels his adversary to just ask him whether he believes there is sufficient evidence to support the view that the agent in question causes the injury.
If you can muster the focus, I urge you to read the post and scan the comment stream. To be sure there are a host of different issues embedded here (e.g., use of confidence intervals vs. statistical significance testing, observational vs. randomized trials, confusion of “statistical significance” with certainty, Bayesian vs. frequentist approaches to hypothesis testing) – all of them critical to evaluating the types of data that are relevant to mass tort litigation involving human health endpoints. And clearly none of them are treated comprehensively. Yet, it is a treasure-trove of information that you and your experts may find informative with respect to your cases.
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