Ben Christopher has a superb piece on history of statistics and the age old debate in statistics – does smoking cause cancer?
In the summer of 1957, Ronald Fisher, one of the fathers of modern statistics, sat down to write a strongly worded letter in defense of tobacco.
The letter was addressed to the British Medical Journal, which, just a few weeks earlier, had taken the editorial position that smoking cigarettes causes lung cancer. According to the journal’s editorial board, the time for amassing evidence and analyzing data was over. Now, they wrote, “all the modern devices of publicity” should be used to inform the public about the perils of tobacco.
According to Fisher, this was nothing short of statistically illiterate fear mongering. Surely the danger posed to the smoking masses was “not the mild and soothing weed,” he wrote, “but the organized creation of states of frantic alarm.”
Fisher was a well-known hothead (and an inveterate pipe smoker), but the letter and the resulting debate, which lasted until his death in 1962, was taken as a serious critique by the scientific community. After all, Ronald A. Fisher had spent much of his career devising ways to mathematically evaluate causal claims—claims exactly like the one that the British Medical Journal was making about smoking and cancer. Along the way, he had revolutionized the way that biological scientists conduct experiments and analyze data.
And yet we know how this debate ends. On one of the most significant public health questions of the 20th century, Fisher got it wrong. But while Fisher was wrong about the specifics, there is still debate over whether he was wrong about the statistics. Fisher never objected to the possibility that smoking caused cancer, only the certainty with which public health advocates asserted this conclusion.
“None think that the matter is already settled,” he insisted in his letter to the British Medical Journal. “Is not the matter serious enough to require more serious treatment?”
The debate over smoking is now over. But on issues ranging from public health, to education, to economics, to climate change, researchers and policymakers still don’t always agree on what sufficiently “serious treatment” looks like.
How can we ever confidently claim that A causes B? How do we weigh the costs of acting too early against the costs of acting too late? And when are we willing to set aside any lingering doubts, stop arguing, and act.
Great read on how scholars argue and make their case..