Quotes About Statistics

In the last fifty years, the ten most extreme days in the financial markets represent half the returns.

During the forty-five months of World War II, the United States lost just under 1 percent of its adult male population; during the Civil War the casualty rate was somewhere between 4 and 5 percent; during the fourteen months of King Philip's War, Plymouth Colony lost close to 8 percent of its men.

A careful investigator, Young interviewed farmers, recording their methods and the size of their harvests. According to his figures, the average yearly harvest in eastern England from an acre of wheat, barley, and oats was between 1,300 and 1,500 pounds. By contrast, an acre of potatoes yielded more than 25,000 pounds—about eighteen times as much.

After Cortés, the population of the entire region collapsed. By 1620–25, it was 730,000, "approximately 3 percent of its size at the time that he first landed." Cook and Borah calculated that the area did not recover its fifteenth-century population until the late 1960s.

As many as one out of every ten people met a violent death in the first millennium A.D., the archaeologist Ian Morris has estimated. Ever since, violence has declined—gradually, then suddenly. In the decades after the Second World War, rates of violent death plunged to the lowest levels ever seen.

because you know all the statistics, nearly 45 per cent of gamers are women, even though if you look at the biz from outside it seems to be focussed on an attention-deficient twelve-year-old male with a breast fixation and a sugar high.

They're nuts. Completely insane! I don't get this gambling thing. Didn't these people study statistics at university? Evidently not

They're nuts. Completely insane! I don't get this gambling thing. Didn't these people study statistics at university?

As a rule of thumb, the sample size must be at least 30 for the central limit theorem to hold true.) This

Fire, knives, automobiles, hair removal cream. Each of these things serves an important purpose. Each one makes our lives better. And each one can cause some serious problems when abused. Now you can add statistics to that list.

Data are to statistics what a good offensive is to a star quarterback.

Statistical inference is really just the marriage of two concepts that we've already discussed: data and probability (with a little help from the central limit theorem).

The good news is that these descriptive statistics give us a manageable and meaningful summary of the underlying phenomenon. That's what this chapter is about. The bad news is that any simplification invites abuse. Descriptive statistics can be like online dating profiles: technically accurate and yet pretty darn misleading.

Here is one of the most important things to remember when doing research that involves regression analysis: Try not to kill anyone. You can even put a little Post-it note on your computer monitor: "Do not kill people with your research.

The belief otherwise is sometimes called "the gambler's fallacy." In fact, if you flip a fair coin 1,000,000 times and get 1,000,000 heads in a row, the probability of getting tails on the next flip is still ½. The

So we simplify. We perform calculations that reduce a complex array of data into a handful of numbers that describe those data

Yes, the probability that five people in the same school or church or workplace will contract the same rare form of leukemia may be one in a million, but there are millions of schools and churches and workplaces

This distinction between correlation and causation is crucial to the proper interpretation of statistical results.

The mean, or average, turns out to have some problems in that regard, namely, that it is prone to distortion by "outliers," which are observations that lie farther from the center.

we have another statistic that also signals the "middle" of a distribution, albeit differently: the median.

For distributions without serious outliers, the median and the mean will be similar.

The central limit theorem tells us that in repeated samples, the difference between the two means will be distributed roughly as a normal distribution.

One fundamental difference between a poll and other forms of sampling is that the sample statistic we care about will be not a mean (e.g., 187 pounds) but rather a percentage or proportion

The standard deviation is the descriptive statistic that allows us to assign a single number to this dispersion around the mean.