Randall Munroe Quotes

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.

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?

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

The big mystery of Big Data is causation versus correlation.

If you're a social scientist worth your salt, you never do a univariate analysis.

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.

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

Beware of averages. The average person has one breast and one testicle.

Being insignificant statistically doesn't mean it's right or wrong. It just means you don't have enough data to show yes or no.

We should be cautious about embracing data before it is published in the academic press, and must always avoid treating correlation as causation.

Don't confuse correlation and causation. Almost all great records eventually dwindle.

The invalid assumption that correlation implies cause is probably among the two or three most serious and common errors of human reasoning.

Statistics always remind me of fellow who drowned in a river where the average depth was only three feet.

If you hear a statistic, you will make up a story to go with it, because our brains are organized on narrative. And you may very well make up a wrong story because you only have one fact, which is a statistic.

All of the studies we do in my group are quantified.

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

We have entered an era where the kinds of data and analyses that people run have gotten incredibly complicated. So you have researchers using sophisticated software programs that they probably don't understand but are generally accepted and everyone [in the field] uses.

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.

The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.

Our polling methodology has gotten outdated, and, in fact, it's not really telling us what it needs to be telling us.

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

Biases and blind spots exist in big data as much as they do in individual perceptions and experiences. Yet there is a problematic belief that bigger data is always better data and that correlation is as good as causation.

The same set of statistics can produce opposite conclusions at different levels of aggregation.

Averages don't always reveal the most telling realities. You know, Shaquille O'Neal and I have an average height of 6 feet.