Quotes About Statistics

our mind is strongly biased toward causal explanations and does not deal well with "mere statistics." When our attention is called to an event, associative memory will look for its cause—more precisely, activation will automatically spread to any cause that is already stored in memory.

Amos and I called our first joint article "Belief in the Law of Small Numbers." We explained, tongue-in-cheek, that "intuitions about random sampling appear to satisfy the law of small numbers, which asserts that the law of large numbers applies to small numbers as well." We also included a strongly worded recommendation that researchers regard their "statistical intuitions with proper suspicion and replace impression formation by computation whenever possible.

Unless you are a professional, however, you may not react very differently to a sample of 150 and to a sample of 3,000. That is the meaning of the statement that "people are not adequately sensitive to sample size.

Amos and I coined the term planning fallacy to describe plans and forecasts that are unrealistically close to best-case scenarios could be improved by consulting the statistics of similar cases

The widespread misunderstanding of randomness sometimes has significant consequences.

whenever accuracy is the goal, bias and noise play the same role in the calculation of overall error.

regression inevitably occurs when the correlation between two measures is less than perfect

On another occasion, Amos and I wondered about the rate of divorce among professors in our university. We noticed that the question triggered a search of memory for divorced professors we knew or knew about, and that we judged the size of categories by the ease with which instances came to mind. We called this reliance on the ease of memory search the availability heuristic.

Statistics produce many observations that appear to beg for causal explanations but do not lend themselves to such explanations. Many facts of the world are due to chance, including accidents of sampling. Causal explanations of chance events are inevitably wrong.

extreme outcomes (both high and low) are more likely to be found in small than in large samples. This explanation is not causal.

For example, if you believe that 3% of graduate students are enrolled in computer science (the base rate), and you also believe that the description of Tom W is 4 times more likely for a graduate student in that field than in other fields, then Bayes's rule says you must believe that the probability that Tom W is a computer scientist is now 11%. If the base rate had been 80%, the new degree of belief would be 94.1%.

Recall that the correlation between two measures—in the present case reading age and GPA—is equal to the proportion of shared factors among their determinants.

Statistics produce many observations that appear to beg for causal explanations but do not lend themselves to such explanations.

Part 2 updates the study of judgment heuristics and explores a major puzzle: Why is it so difficult for us to think statistically? We easily think associatively, we think metaphorically, we think causally, but statistics requires thinking about many things at once, which is something that System 1 is not designed to do.

we are statistically punished for being nice

Allowing the observers to influence each other effectively reduces the size of the sample, and with it the precision of the group estimate.

even compelling causal statistics will not change long-held beliefs or beliefs rooted in personal experience.

Scholars in other disciplines found it useful, and the ideas of heuristics and biases have been used productively in many fields, including medical diagnosis, legal judgment, intelligence analysis, philosophy, finance, statistics, and military strategy.

even compelling causal statistics will not change long-held beliefs or beliefs rooted in personal experience. On the other hand, surprising individual cases have a powerful impact and are a more effective tool for teaching psychology because the incongruity must be resolved and embedded in a causal story.

for a large majority of fund managers, the selection of stocks is more like rolling dice than like playing poker. Typically at least two out of every three mutual funds underperform the overall market in any given year.

We found that American women spent about 19% of the time in an unpleasant state, somewhat higher than French women (16%) or Danish women (14%).

I had stumbled onto a significant fact of the human condition: the feedback to which life exposes us is perverse. Because we tend to be nice to other people when they please us and nasty when they do not, we are statistically punished for being nice and rewarded for being nasty.

Relying on causal thinking about a single case is a source of predictable errors. Taking the statistical view, which we will also call the outside view, is a way to avoid these errors.

the preference for causal thinking also contributes to the neglect of noise as a source of error, because noise is a fundamentally statistical notion.