Stephen Jay Gould Quotes

But to measure cause and effect... you must ensure that a simple correlation, however tempting it may be, is not mistaken for a cause. In the 1990s the stork population of Germany increased and the German at-home birth rate rose as well. Shall we credit storks for airlifting the babies?

If two or more instances of the phenomenon under investigation have only one circumstance in common, the circumstance in which alone all the instances agree is the cause (or effect) of the given phenomenon.

Logic is a poor model of cause and effect.

It doesn't take many observations to think you've spotted a trend, and it's probably not a trend at all.

There is no more dangerous error than that of mistaking the effect for the cause.

All too often when liberals cite statistics, they forget the statisticians' warning that correlation is not causation.

Correlation doesn’t imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing ‘look over there.’

Correlations are not explanations and besides, they can be as spurious as the high correlation in Finland between foxes killed and divorces.

One of the first things taught in introductory statistics textbooks is that correlation is not causation. It is also one of the first things forgotten.

Much of what I do in my job is think about whether relationships we see in data are causal, as opposed to just reflecting correlations. It's exactly these issues which come up in evaluating studies in public health.

You're trying too hard to find a correlation here. You don't know these people, you don't know what they intended. You try to compile statistics and correlate them to a result that amounts to nothing more than speculation.

An error is the more dangerous in proportion to the degree of truth which it contains.

When you try to predict future E.R.A.'s with past E.R.A.'s, you're making a mistake.

But what if our intelligence is wrong?

Men are more apt to be mistaken in their generalizations than in their particular observations.

Results rarely specify their causes unambiguously…. If we are forced to infer a process only from its … results, then we are usually stymied or reduced to speculation about probabilities.

History is the unfolding of miscalculations

In the fabric of human events, one thing leads to another. Every mistake is in a sense the product of all the mistakes that have gone before it, from which fact it derives a sort of cosmic forgiveness; and at the same time every mistake is in a sense the determinant of all the mistakes of the future, from which it derives a sort of cosmic unforgivableness.

We must prove design before we can infer a designer.

Those who use 'Correlation is not the same as causation' as a magic incantation to dismiss all fact-using professions are fools holding a lit match in one hand and an open gas can in the other, screaming, 'One has nothing to do with the other!'

I'm very familiar with how people can confuse correlation with causation.

The big mystery of Big Data is causation versus correlation.

Logic cannot model causal systems, and paradox is generated when time is ignored [as in logic].

Everything has a cause.

Innocence lost is not easily regained. The designer simply cannot predict the problems people will have, the misinterpretations that will arise, and the errors that will get made.