In a nutshell: if you look harder than the quantity of data supports, you will find a pattern that "fits".
I have just heard of Bonferroni's Principle, under that label, but this is a key issue and well deserving of a name. The meaningfulness of an answer depends on the evidence that stands behind it. Bonferroni's Principle, that you will "find" something if you look hard enough, underscores conspiracy theories, much of social and economic empirical findings, and data-mining in general. We have all done this ourselves, and have seen others do this. Our brains are pattern matching and story telling machines - we will find a pattern and a story to fit what we see (barring complete lack of imagination). The question is if the pattern and story is legitimate or not: is it overly fragile and "overfit" to the data? Did we use a "training set" to come up with our story, and a "test set" to test it out? Conspiracy theories are notoriously brittle - they strain credulity, even in the case in question, and if you take the theory and look at any other piece of life it falls apart: the theory over fit some specific event, often with liberal doses of biased assumptions included, and makes no sense once you expose it to new, fresh data.
In large part science is about Bonferroni's Principle - we want to learn how to pick out legitimate patterns and stories from what we observe, for several reasons (we love stories and patterns, we find our lives improve if we have accurate and interesting ones, we can get social recognition from a group of likeminded searchers in our quest, etc.). The scientific method is all about good story telling - by asking (good) questions and (honestly) listening to the answers we get a good, and more true than not, story.
Our brains are amazing pattern finding and story weaving organs, and our job is to consciously test the stories and patterns that are being spun: we want to help our brains in its "brain storming" ways, by adding even more possibilities, and we need to help with critical analysis of the conclusions we jump to.
Examples:
A related sub-effect is the Barnum effect, which is Bonferroni's Principle applied to personality categories: people will agree with categories they are placed on, and believe that the categories are illuminating, for example astrological signs. Again, until recently, I did not know there was a name for this effect - but have seen it is strong in many people. I had a roommate who was convinced that horoscopes where accurate in describing people, as a test I read two signs and had him pick which one was his (e.g. his sign, and as random as one I could pick as another option, read in random order) and every week for a month I had him select his using a weekly 'scope he thought was good. Surprisingly he picked wrong every time (less than 10% odds, if his selection was random).
Townhall meeting: otherwise known as sample bias. Only those in the tails of the distribution of people effected by some decision will bother to show up. Most political decisions are structured so a small subset gain significant advantage, with a cost borne by everyone else. This small-many ratio ensures a small cost (so most likely not worth showing up) and a huge gain (so if you stand gain you will show up). So the tail of those to gain will be in the house, and perhaps some "anti" wackos. Result: "widespread support" that is underscored by only an embittered small scattering of crazies who oppose. The choice is clear...
Health science. Are supplements good for you? Who knows. We do know that those who take supplements tend to care deeply about health, so if we just compare these people against the average (i.e. unhealthy subjects) are we comparing difference in taking supplements, or differences in: exercise, positive attitude, smoking, drinking, etc. We are testing them all. So unless supplements have huge negative effects we will see a positive effect. Sadly, many studies have poor protocols such as using different sample groups - the study will find a difference between groups, but what causes this? In general, the scientific scam known as "significance" is at play here. Passing a p-test deems a hypothesis "significant", but Bonferroni's Principle tells us if we look hard enough we can always pass a p-test. Further, the use of "significant" hijacks our brains by misusing our filters. This is unfortunate, as the field of health is important and people make choices based on weak "science".
Lowdown:
- Quality must have quantity as support.
- Use simple tests to check your data (representative?) and story (legitimate?).
- Much of "science", particularly in social and health fields where biases and feelings and complexity runs high, falls prey to Bonferroni's Principle and is a scam. Cavet emporor.
- Having a label for something is nice - drop Bonferroni's Principle on people. Like an idiom a label for a developed idea allows rapid, deep, incisive discussion. In learning about science and human thinking Bonferroni's Principle should be one of the key points discussed.
