How can correlation be misperceived as causation

Sign In or Create an Account. Sign In. Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents Abstract. The Model. General Results. Two Applications. But the question of cause, which has haunted science and philosophy from their earliest days, still dogs our heels for numerous reasons.

Humans are evolutionarily predisposed to see patterns and psychologically inclined to gather information that supports pre-existing views, a trait known as confirmation bias. We confuse coincidence with correlation and correlation with causality.

For A to cause B, we tend to say that, at a minimum, A must precede B, the two must covary vary together , and no competing explanation can better explain the covariance of A and B. Taken alone, however, these three requirements cannot prove cause; they are, as philosophers say, necessary but not sufficient.

In any case, not everyone agrees with them. Speaking of philosophers, David Hume argued that causation doesn't exist in any provable sense. Karl Popper and the Falsificationists maintained that we cannot prove a relationship, only disprove it, which explains why statistical analyses do not try to prove a correlation; instead, they pull a double negative and disprove that the data are uncorrelated, a process known as rejecting the null hypothesis.

With such considerations in mind, scientists must carefully design and control their experiments to weed out bias, circular reasoning, self-fulfilling prophecies and hidden variables. They must respect the requirements and limitations of the methods used, draw from representative samples where possible, and not overstate their results. People are a pain to research. They react not only to the stimulus you're studying but also to the experiment itself.

Researchers today try to design experiments to control for such factors, but such was not always the case. Take the Hawthorne Works in Cicero, Ill. In a series of experiments from , researchers studied the worker productivity effects associated with altering the Illinois factory's environment, including changing light levels, tidying up the place and moving workstations around. Just when they thought they were on to something, they noticed a problem: The observed increases in productivity flagged almost as soon as the researchers left the works, indicating that the workers' knowledge of the experiment, not the researchers' changes, had fueled the boost.

Researchers still call this phenomenon the Hawthorne Effect. A related concept, the John Henry effect , occurs when members of a control group try to beat the experimental group by kicking their efforts into overdrive. They need not know about the experiment; they need only see one group receive new tools or additional instruction. Like the steel-driving man of legend, they want to prove their capabilities and earn respect [sources: Saretsky ; Vogt ].

The titular characters of Tom Stoppard's film "Rosencrantz and Guildenstern Are Dead" begin the film baffled, confused and finally frightened as each of consecutive flips of a coin comes up heads. Guildenstern's explanations of this phenomenon range from time loops to "a spectacular vindication of the principle that each individual coin, spun individually, is as likely to come down heads as tails Evolution wired humans to see patterns, and our ability to properly process that urge seems to short-circuit the longer we spend gambling.

We can rationally accept that independent events like coin flips keep the same odds no matter how many times you perform them. But we also view those events, less rationally, as streaks, making false mental correlations between randomized events.

Viewing the past as prelude, we keep thinking the next flip ought to be tails. Statisticians call this the gambler's fallacy , aka the Monte Carlo fallacy , after a particularly illustrative example occurring in that famed Monaco resort town.

During the summer of , bettors watched in increasing amazement as a casino's roulette wheel landed on black 26 times in a row. Inflamed by the certainty that red was "due," the punters kept plunking down their chips. The casino made a mint [sources: Lehrer ; Oppenheimer and Monin ; Vogt ]. No discussion of streaks, magical thinking or false causation would be complete without a flip through the sports pages.

Stellar sports seasons arise from such a mysterious interplay of factors -- natural ability, training, confidence, the occasional X factor -- that we imagine patterns in performance, even though studies repeatedly reject streak shooting and "successful" superstitions as anything more than imaginary.

The belief in streaks or slumps implies that success "causes" success and failure "causes" failure or, perhaps more reasonably, that variation in some common factor, such as confidence, causes both.

But study after study fails to bear this out [sources: Gilovich et al. The same holds true for superstitions , although that did not stop the Cleveland Indians' Kevin Rhomberg from refusing to make right turns while on the field, or prevent Ottawa Senators center Bruce Gardiner from dunking his hockey stick in the toilet to break the occasional slump [source: Trex ].

The sophomore slump, too, typically arises from a too-good first year. Performance swings tend to even out in the long run, a phenomenon statisticians call regression toward the mean. In sports, this averaging out is aided by the opposition, which adjusts to counter the new player's successful skill set. Randomized controlled trials are the gold standard in statistics, but sometimes -- in epidemiology, for example -- ethical and practical considerations force researchers to analyze available cases.

Unfortunately, such observational studies risk bias, hidden variables and, worst of all, a study group that might not reflect the population as a whole. Sophisticated statistical methods are needed to determine just how much clustering is required to deduce that something in that area might be causing the illness.

Unfortunately, any cluster at all — even a non-significant one — makes for an easy and at first glance, compelling news headline. Statistical analysis, like any other powerful tool, must be used very carefully — and in particular, one must always be careful when drawing conclusions based on the fact that two quantities are correlated.

Instead, we must always insist on separate evidence to argue for cause-and-effect — and that evidence will not come in the form of a single statistical number. Seemingly compelling correlations, say between given genes and schizophrenia or between a high fat diet and heart disease, may turn out to be based on very dubious methodology.

We are perhaps as a species cognitively ill prepared to deal with these issues. The bad news is that our evolution equipped us to live in small, stable, hunter-gatherer societies. We are Pleistocene people, but our languaged brains have created massive, multicultural, technologically sophisticated and rapidly changing societies for us to live in.

In consequence, we must constantly resist the temptation to see meaning in chance and to confuse correlation and causation. This article is part of a series on Understanding Research. The minimum wage, for example, is a hotly debated subject due to the challenge in acquiring data that can express the causal effects of changes in the minimum wage.

A study published in the American Journal of Epidemiology in found an association between Facebook use and reduced well-being.

The study does not tell us whether Facebook use is indeed causing depression. It is equally as plausible to imagine depression to cause increased Facebook usage. The problem is, a news event correlating with stock prices does not imply that the event actually caused the changes in the market. Does Anchoring Work in the Courtroom? This article touches on many of the quandaries and challenges in behavioral science research in meeting the demands of proving causality.

Correlation vs Causation. Theory, meet practice TDL is an applied research consultancy. Key Terms Correlation : An association between two pieces of data. History Around , the English polymath Sir Francis Galton began to suspect a divergence from statistics and causation.

Consequences The consequences of mistaking causation for correlation are vast. Controversies As one can imagine, the correlation versus causation space can be a tinderbox for igniting controversies. Case Studies Facebook and mental health A study published in the American Journal of Epidemiology in found an association between Facebook use and reduced well-being.

Related resources Does Anchoring Work in the Courtroom? The Game of Life: Discussing Determinism in Behavioral Science This article touches on many of the quandaries and challenges in behavioral science research in meeting the demands of proving causality.

Sources Pearl, J. The book of why: the new science of cause and effect. Basic Books. Archer, E.