Naked Statistics Quotes

“It’s easy to lie with statistics, but it’s hard to tell the truth without them.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“The greatest risks are never the ones you can see and measure, but the ones you can’t see and therefore can never measure. The ones that seem so far outside the boundary of normal probability that you can’t imagine they could happen in your lifetime—even though, of course, they do happen, more often than you care to realize.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“So it is with statistics; no amount of fancy analysis can make up for fundamentally flawed data. Hence the expression “garbage in, garbage out.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“Probability doesn’t make mistakes; people using probability make mistakes.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“Descriptive statistics exist to simplify, which always implies some loss of nuance or detail.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“In the end, I hope to persuade you of the observation first made by Swedish mathematician and writer Andrejs Dunkels: It’s easy to lie with statistics, but it’s hard to tell the truth without them.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“Regression analysis is the hydrogen bomb of the statistics arsenal.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“the most dangerous kind of job stress stems from having “low control” over one’s responsibilities.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“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.” Because some very smart people have inadvertently violated that rule.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“Data are to statistics what a good offensive is to a star quarterback.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“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.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“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).”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“VaR has been called “potentially catastrophic,” “a fraud,” and many other things not fit for a family book about statistics like this one. In particular, the model has been blamed for the onset and severity of the financial crisis. The primary critique of VaR is that the underlying risks associated with financial markets are not as predictable as a coin flip or even a blind taste test between two beers. The false precision embedded in the models created a false sense of security. The VaR was like a faulty speedometer, which is arguably worse than no speedometer at all. If you place too much faith in the broken speedometer, you will be oblivious to other signs that your speed is unsafe. In contrast, if there is no speedometer at all, you have no choice but to look around for clues as to how fast you are really going.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“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.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“Statistics is like a high-caliber weapon: helpful when used correctly and potentially disastrous in the wrong hands.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“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.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“(As a rule of thumb, the sample size must be at least 30 for the central limit theorem to hold true.) This”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“The [Value at Risk model] was like a faulty speedometer, which is arguably worse than no speedometer at all. If you place too much faith in the broken speedometer, you will be oblivious to other signs that your speed is unsafe. In contrast, if there is no speedometer at all, you have no choice but to look around for clues as to how fast you are really going.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“Correlation does not equal causation”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“Does putting more police officers on the street deter crime?”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“Stress is not associated with major responsibilities that will kill you; it is the stress associated with being told what to do while having little say in how or when it gets done.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“Most of the studies that you read about in the newspaper are based on regression analysis.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“The first intriguing statistical question is whether we are experiencing an epidemic of autism, an “epidemic of diagnosis,” or some combination of the two?”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“Gladwell’s central premise was that both professional football and dog fighting are inherently devastating to the participants.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“WHAT IS THE FUTURE OF FOOTBALL? In 2009, Malcolm Gladwell posed a question in a New Yorker article that first struck me as needlessly sensationalist and provocative: How different are dog fighting and football?”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“If Dale and Krueger had found that students who attend a highly selective school had higher lifetime earnings than students who were accepted at such a school but went to a less selective college instead, we still could not be certain whether the difference was due to the selective school or to the kind of student who opted to attend such a school when given a choice. This potential bias turns out to be unimportant in the Dale and Krueger study, however, because of its direction. Dale and Krueger find that the students who attended highly selective schools did not earn significantly more in life than students who were accepted but went elsewhere despite the fact that the students who declined to attend a highly selective school may have had attributes that caused them to earn less in life apart from their education. If anything, the bias here causes the findings to overstate the pecuniary benefits of attending a highly selective college—which turn out to be insubstantial anyway.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“The purpose of any program evaluation is to provide some kind of counterfactual against which a treatment or intervention can be measured. In the case of a randomized, controlled experiment, the control group is the counterfactual. In cases where a controlled experiment is impractical or immoral, we need to find some other way of approximating the counterfactual. Our understanding of the world depends on finding clever ways to do that.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“Nonequivalent control. Sometimes the best available option for studying a treatment effect is to create nonrandomized treatment and control groups. Our hope/expectation is that the two groups are broadly similar even though circumstances have not allowed us the statistical luxury of randomizing. The good news is that we have a treatment and a control group. The bad news is that any nonrandom assignment creates at least the potential for bias. There may be unobserved differences between the treatment and control groups related to how participants are assigned to one group or the other. Hence the name “nonequivalent control.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“Will prayer be the cost-effective solution to America’s health care challenges? Probably not. The researchers did not find any difference in the rate of complications within thirty days of surgery for those who were offered prayers compared with those who were not. Critics of the study pointed out a potential omitted variable: prayers coming from other sources. As the New York Times summarized, “Experts said the study could not overcome perhaps the largest obstacle to prayer study: the unknown amount of prayer each person received from friends, families, and congregations around the world who pray daily for the sick and dying.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data

“The beauty of randomization is that it will generally distribute the non-treatment-related variables more or less evenly between the two groups—both the characteristics that are obvious, such as sex, race, age, and education and the nonobservable characteristics that might otherwise mess up the results.”
― Charles Wheelan, Naked Statistics: Stripping the Dread from the Data