From time to time I have been known to fill this space with pet peeves. And I frequently write about evidence-based decision-making as it applies to specific topics or news stories.
I have never put the two together, however, even though I have plenty of critical thinking pet peeves.
Correlation, causation
It never ceases to amaze me how frequently people confuse these two concepts.
At its simplest it goes something like: Joe had a cold yesterday. I have a cold today therefore Joe gave me his cold.
Of course, the two don't necessarily follow. It is possible, but I could have caught the cold from myriad different sources.
At its worst it can result in great harm. The classic example of this is the anti-vaccination movement which stemmed from an erroneous correlation between vaccines and autism that was taken advantage of by an unscrupulous doctor.
It can be a starting point as in the vaccination-autism debate. Scientists set out to test whether the correlation did, in fact, point to a cause. Study after study has totally debunked the claim and yet we are now seeing childhood diseases that were all but eradicated making a comeback because of fear generated by mistaking correlation with causation.
Exceptions
Unfortunately, I still hear people casting doubt on certain conclusions for which the overwhelming scientific consensus is ironclad.
Just the other day, in fact, someone told me they were unconvinced that smoking was bad for human health because he knew a guy who had smoked 'a pack a day all his life' and lived to be 95.
One person, or even hundreds beating the odds does not negate the evidence that smoking kills.
Furthermore, how do we know this man would not have lived to be 115 or set a new record for human longevity if he had not smoked?
Anecdotes
The above story is an example of an anecdote, an anecdote about anecdotes, if I may. I treat them separately here because as a category of evidence, it is the most unreliable, but also frequently the most compelling to people.
It is not surprising, perhaps, because we instinctively trust the experience of others. It is how we learn from the day we are born.
And, from an evolutionary point of view, it is probably just as well. If my wife eats the leaves of the plant with the red berries and then gets ill, I'm probably better off just to avoid that plant.
Humans are also hardwired with curiosity though and at some point, someone decided that we could test the assumption that the plant with the red berries makes people sick with a double-blind controlled study.
Anecdotal evidence is not completely useless; the problem arises when people trust anecdotes above other more reliable evidence.
Outliers
This does not just peeve me, it makes me angry and I hear it all the time.
There are credible scientists (credible data) that shows (evolution, climate change, declining biodiversity, vaccinations etc.) is wrong.
To put it bluntly: No there is not.
There will always be outliers. One or two people who buck the consensus usually because they have been specifically trained to do so, or to make money, or to promote some other ideology or a variety of other reasons.
There are even creationist schools in the United States that have somehow managed to get accredited to issue science degrees, but do not teach real science.
Outliers also exist in data. That one point that is wildly inconsistent with the trend. We do not ignore them, but they do not get the same weight as
Confirmation bias
Another tendency of human beings that is almost unavoidable is the tendency to seek out, or selectively see, those pieces of information that affirm what we already believe.
This is precisely why the scientific method has been developed to protect the integrity of experiments and results from the 'observer effect.'
This is the principle that certain measurements cannot be made without influencing the results. There is always a certain amount of uncertainty in any human activity, but legitimate scientists, political analysts, journalists and others who are expected to be objective, do everything in their power to avoid as much uncertainty as possible.
Too often people start with the conclusion they want and work the evidence backwards to support it.
