thesis of outliers

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By David Leonhardt

• Nov. 28, 2008

In 1984, a young man named Malcolm graduated from the University of Toronto and moved to the United States to try his hand at journalism. Thanks to his uncommonly clear writing style and keen eye for a story, he quickly landed a job at The Washington Post. After less than a decade at The Post, he moved up to the pinnacle of literary journalism, The New Yorker. There, he wrote articles full of big ideas about the hidden patterns of ordinary life, which then became grist for two No. 1 best-selling books. In the vast world of nonfiction writing, he is as close to a singular talent as exists today.

Or at least that’s one version of the story of Malcolm Gladwell. Here is another:

In 1984, a young man named Malcolm graduated from the University of Toronto and moved to the United States to try his hand at journalism. No one could know it then, but he arrived with nearly the perfect background for his time. His mother was a psychotherapist and his father a mathematician. Their professions pointed young Malcolm toward the behavioral sciences, whose popularity would explode in the 1990s. His mother also just happened to be a writer on the side. So unlike most children of mathematicians and therapists, he came to learn, as he would later recall, “that there is beauty in saying something clearly and simply.” As a journalist, he plumbed the behavioral research for optimistic lessons about the human condition, and he found an eager audience during the heady, proudly geeky ’90s. His first book, “The Tipping Point,” was published in March 2000, just days before the Nasdaq peaked.

These two stories about Gladwell are both true, and yet they are also very different. The first personalizes his success. It is the classically American version of his career, in that it gives individual characteristics — talent, hard work, Horatio Alger-like pluck — the starring role. The second version doesn’t necessarily deny these characteristics, but it does sublimate them. The protagonist is not a singularly talented person who took advantage of opportunities. He is instead a talented person who took advantage of singular opportunities.

Gladwell’s latest book, “Outliers,” is a passionate argument for taking the second version of the story more seriously than we now do. “It is not the brightest who succeed,” Gladwell writes. “Nor is success simply the sum of the decisions and efforts we make on our own behalf. It is, rather, a gift. Outliers are those who have been given opportunities — and who have had the strength and presence of mind to seize them.”

He doesn’t actually tell his own life story in the book. (But he lurks offstage, since he does describe the arc of his mother’s Jamaican family.) Instead, he tells other success stories, often using the device of back-to-back narratives. He starts with a tale of individual greatness, about the Beatles or the titans of Silicon Valley or the enormously successful generation of New York Jews born in the early 20th century. Then he adds details that undercut that tale.

So Bill Gates is introduced as a young computer programmer from Seattle whose brilliance and ambition outshine the brilliance and ambition of the thousands of other young programmers. But then Gladwell takes us back to Seattle, and we discover that Gates’s high school happened to have a computer club when almost no other high schools did. He then lucked into the opportunity to use the computers at the University of Washington, for hours on end. By the time he turned 20, he had spent well more than 10,000 hours as a programmer.

At the end of this revisionist tale, Gladwell asks Gates himself how many other teenagers in the world had as much experience as he had by the early 1970s. “If there were 50 in the world, I’d be stunned,” Gates says. “I had a better exposure to software development at a young age than I think anyone did in that period of time, and all because of an incredibly lucky series of events.” Gates’s talent and drive were surely unusual. But Gladwell suggests that his opportunities may have been even more so.

Many people, I think, have an instinctual understanding of this idea (even if Gladwell, in the interest of setting his thesis against conventional wisdom, doesn’t say so). That’s why parents spend so much time worrying about what school their child attends. They don’t really believe the child is so infused with greatness that he or she can overcome a bad school, or even an average one. And yet when they look back years later on their child’s success — or their own — they tend toward explanations that focus on the individual. Devastatingly, if cheerfully, Gladwell exposes the flaws in these success stories we tell ourselves.

The book’s first chapter explores the anomaly of hockey players’ birthdays. In many of the best leagues in the world, amateur or professional, roughly 40 percent of the players were born in January, February or March, while only 10 percent were born in October, November or December. It’s a profoundly strange pattern, with a simple explanation. The cutoff birth date for many youth hockey leagues is Jan. 1. So the children born in the first three months of the year are just a little older, bigger and stronger than their peers. These older children are then funneled into all-star teams that offer the best, most intense training. By the time they become teenagers, their random initial advantage has turned into a real one.

At the championship game of the top Canadian junior league, Gladwell interviews the father of one player born on Jan. 4. More than half of the players on his team — the Medicine Hat Tigers — were born in January, February or March. But when Gladwell asks the father to explain his son’s success, the calendar has nothing to do with it. He instead mentions passion, talent and hard work — before adding, as an aside, that the boy was always big for his age. Just imagine, Gladwell writes, if Canada created another youth hockey league for children born in the second half of the year. It would one day find itself with twice as many great hockey players.

“Outliers” has much in common with Gladwell’s earlier work. It is a pleasure to read and leaves you mulling over its inventive theories for days afterward. It also, unfortunately, avoids grappling in a few instances with research that casts doubt on those theories. (Gladwell argues that relatively older children excel not only at hockey but also in the classroom. The research on this issue, however, is decidedly mixed.) This is a particular shame, because it would be a delight to watch someone of his intellect and clarity make sense of seemingly conflicting claims.

For all these similarities, though, “Outliers” represents a new kind of book for Gladwell. “The Tipping Point” and “Blink,” his second book, were a mixture of social psychology, marketing and even a bit of self-help. “Outliers” is far more political. It is almost a manifesto. “We look at the young Bill Gates and marvel that our world allowed that 13-year-old to become a fabulously successful entrepreneur,” he writes at the end. “But that’s the wrong lesson. Our world only allowed one 13-year-old unlimited access to a time-sharing terminal in 1968. If a million teenagers had been given the same opportunity, how many more Microsofts would we have today?”

After a decade — and, really, a generation — in which this country has done fairly little to build up the institutions that can foster success, Gladwell is urging us to rethink. Once again, his timing may prove to be pretty good.

The Story of Success.

By Malcolm Gladwell.

Illustrated. 309 pp. Little, Brown & Company. $27.99

David Leonhardt is an economics columnist for The Times.

by Malcolm Gladwell

Outliers themes.

As indicated by the subtitle of Outliers ( The Story of Success ), success is the book's primary theme. Much of Gladwell's analysis involves profiling brilliant, dynamic, or at least ambitious and promising individuals, often with a the goal of defining the specific factors that made these individuals successful. However, Gladwell is equally clear about what does not entail success. Intelligence measures such as IQ cannot be firmly linked to exceptional success, whereas hard work, lucky circumstances, and supportive communities clearly help an individual to succeed.

Much of the discussion in Outliers hinges on the ability of culture to instill certain values in successful (and in some cases unsuccessful) individuals. For instance, the cultures of Jewish garment workers and Asian rice farmers both created a sense of meaningful work that exerted a positive influence on both the people who directly participated in these cultures and their descendants. Nonetheless, cultural legacies can also be negative: as Gladwell explains, catastrophes such as the Korean Air plane crashes have their roots in culturally dictated methods of communication.

Opportunity

Gladwell argues throughout Outliers that the opportunity to practice and succeed may outweigh factors such as IQ, originality, and perhaps even culture of origin in determining success. Such opportunity can take a variety of forms, from access to a large number of practice hours to the psychological advantages provided by a higher-income family background. Ultimately, though, opportunity can be created even for those who start off with clear disadvantages. Outliers , after all, includes the story of Marita , a girl from a socioeconomically disadvantaged background who is granted the opportunity to succeed by becoming a KIPP student.

Gladwell's account of success includes an analysis of the famous "10,000-hours rule," which states that 10,000 hours spent practicing and refining a skill will lead to mastery of that skill. Using examples such as the Beatles and Bill Gates , Gladwell shows how extremely high-achieving individuals required access to constant practice in order to refine their abilities, regardless of the chosen field or objective. Presumably, some of the other individuals Gladwell profiles (from corporate lawyers to hockey players) required similarly rigorous practice in order to attain true prowess.

Self-Awareness

One of the underlying ideas in Outliers is that individuals are not always fully aware of why they succeed--or why they fail. A large part of Gladwell's mission is to demystify these issues by alerting his readers to the influence of opportunity and cultural legacy in determining success. Limited awareness of cultural legacies can be a major reason for failure, as in the case of the Korean Air pilots, who were unaware of their own culturally dictated deficiencies in communication. Yet such limited awareness can be corrected: once Korean Air became conscious of ingrained flaws, it was able to correct detrimental communication practices.

Although Gladwell often emphasizes the role of practice and initiative, he does not deny the role of luck in determining success. After all, a hockey player born in the right month will be granted built-in advantages of increased practice and attention; something similar could be said of a corporate lawyer, industrial tycoon, or software engineer born in the right year. Success, according to Gladwell, is in part the product of numerous arbitrary factors (ethnicity and social class among them) that make remarkable opportunities possible.

Gladwell does discuss the idea of genius in Outliers , but he does so in a manner that reveals his thorough skepticism towards the linkage between genius and success. Traditional measures of genius (such as IQ) do not reliably predict real-world achievement. Moreover, it is possible for an individual of clear genius to have relatively few accomplishments (as in the case of Chris Langan) or relatively many (as in the case of Robert Oppenheimer ). It is necessary for genius to be accompanied by the other factors that Gladwell cites, such as opportunity and community support, for a man or woman of genius to be successful.

Outliers Questions and Answers

The Question and Answer section for Outliers is a great resource to ask questions, find answers, and discuss the novel.

Outliers Questions

Yes, I believe that certain people are born with certain talents.

Gladwell says that in the end, preparation becomes plays a bigger role than talent. The "best" practice more than anyone else. Thus, someone with innate talent must still prepare or...

What is accumulative advantage?

All the advantages that one gets in life leading to success like family wealth, opportunity, race........All of these advantages accumulate.

What is the magic number for mastering a specific skill?

That would be 10000 hours.

Study Guide for Outliers

Outliers study guide contains a biography of Malcolm Gladwell, literature essays, quiz questions, major themes, characters, and a full summary and analysis.

• About Outliers
• Outliers Summary
• Character List

Essays for Outliers

Outliers essays are academic essays for citation. These papers were written primarily by students and provide critical analysis of Outliers by Malcolm Gladwell.

• The 10,000 Hour Rule in Outliers
• Malcolm Gladwell's "Small Change": A Rhetorical Analysis

Lesson Plan for Outliers

• About the Author
• Study Objectives
• Common Core Standards
• Introduction to Outliers
• Relationship to Other Books
• Bringing in Technology
• Notes to the Teacher
• Related Links
• Outliers Bibliography

Wikipedia Entries for Outliers

• Introduction

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• Knowledge Base
• How to Find Outliers | 4 Ways with Examples & Explanation

How to Find Outliers | 4 Ways with Examples & Explanation

Published on November 30, 2021 by Pritha Bhandari . Revised on January 17, 2024.

Outliers are extreme values that differ from most other data points in a dataset. They can have a big impact on your statistical analyses and skew the results of any hypothesis tests .

It’s important to carefully identify potential outliers in your dataset and deal with them in an appropriate manner for accurate results.

Sorting method

• Data visualization method
• Statistical tests ( z scores)

Interquartile range method

Table of contents

What are outliers, four ways of calculating outliers, example: using the interquartile range to find outliers, dealing with outliers, other interesting articles, frequently asked questions about outliers.

Outliers are values at the extreme ends of a dataset.

Some outliers represent true values from natural variation in the population. Other outliers may result from incorrect data entry, equipment malfunctions, or other measurement errors .

An outlier isn’t always a form of dirty or incorrect data, so you have to be careful with them in data cleansing . What you should do with an outlier depends on its most likely cause.

True outliers

True outliers should always be retained in your dataset because these just represent natural variations in your sample .

True outliers are also present in variables with skewed distributions where many data points are spread far from the mean in one direction. It’s important to select appropriate statistical tests or measures when you have a skewed distribution or many outliers.

Other outliers

Outliers that don’t represent true values can come from many possible sources:

• Measurement errors
• Data entry or processing errors
• Unrepresentative sampling

For one of the participants, you accidentally start the timer midway through their sprint. You record this timing as their running time.

This type of outlier is problematic because it’s inaccurate and can distort your research results .

In practice, it can be difficult to tell different types of outliers apart. While you can use calculations and statistical methods to detect outliers, classifying them as true or false is usually a subjective process.

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You can choose from several methods to detect outliers depending on your time and resources.

You can sort quantitative variables from low to high and scan for extremely low or extremely high values. Flag any extreme values that you find.

This is a simple way to check whether you need to investigate certain data points before using more sophisticated methods.

You sort the values from low to high and scan for extreme values.

Using visualizations

You can use software to visualize your data with a box plot, or a box-and-whisker plot, so you can see the data distribution at a glance. This type of chart highlights minimum and maximum values (the range ), the median , and the interquartile range for your data.

Many computer programs highlight an outlier on a chart with an asterisk, and these will lie outside the bounds of the graph.

Statistical outlier detection

Statistical outlier detection involves applying statistical tests or procedures to identify extreme values.

You can convert extreme data points into z scores that tell you how many standard deviations away they are from the mean.

If a value has a high enough or low enough z score, it can be considered an outlier. As a rule of thumb, values with a z score greater than 3 or less than –3 are often determined to be outliers.

Using the interquartile range

The interquartile range (IQR) tells you the range of the middle half of your dataset. You can use the IQR to create “fences” around your data and then define outliers as any values that fall outside those fences.

This method is helpful if you have a few values on the extreme ends of your dataset, but you aren’t sure whether any of them might count as outliers.

• Sort your data from low to high
• Identify the first quartile (Q1), the median, and the third quartile (Q3).
• Calculate your IQR = Q3 – Q1
• Calculate your upper fence = Q3 + (1.5 * IQR)
• Calculate your lower fence = Q1 – (1.5 * IQR)
• Use your fences to highlight any outliers, all values that fall outside your fences.

Your outliers are any values greater than your upper fence or less than your lower fence.

We’ll walk you through the popular IQR method for identifying outliers using a step-by-step example.

Your dataset has 11 values. You have a couple of extreme values in your dataset, so you’ll use the IQR method to check whether they are outliers.

Step 1: Sort your data from low to high

First, you’ll simply sort your data in ascending order.

Step 2: Identify the median, the first quartile (Q1), and the third quartile (Q3)

The median is the value exactly in the middle of your dataset when all values are ordered from low to high.

Since you have 11 values, the median is the 6th value. The median value is 31.

Next, we’ll use the exclusive method for identifying Q1 and Q3. This means we remove the median from our calculations.

The Q1 is the value in the middle of the first half of your dataset, excluding the median. The first quartile value is 25.

Your Q3 value is in the middle of the second half of your dataset, excluding the median. The third quartile value is 41.

Step 3: Calculate your IQR

The IQR is the range of the middle half of your dataset. Subtract Q1 from Q3 to calculate the IQR.

Step 4: Calculate your upper fence

The upper fence is the boundary around the third quartile. It tells you that any values exceeding the upper fence are outliers.

Step 5: Calculate your lower fence

The lower fence is the boundary around the first quartile. Any values less than the lower fence are outliers.

Step 6: Use your fences to highlight any outliers

Go back to your sorted dataset from Step 1 and highlight any values that are greater than the upper fence or less than your lower fence. These are your outliers.

• Upper fence = 63.5
• Lower fence = 3.5

You find one outlier, 64, in your dataset.

Once you’ve identified outliers, you’ll decide what to do with them. Your main options are retaining or removing them from your dataset. This is similar to the choice you’re faced with when dealing with missing data .

For each outlier, think about whether it’s a true value or an error before deciding.

• Does the outlier line up with other measurements taken from the same participant?
• Is this data point completely impossible or can it reasonably come from your population ?
• What’s the most likely source of the outlier? Is it a natural variation or an error?

In general, you should try to accept outliers as much as possible unless it’s clear that they represent errors or bad data.

Retain outliers

Just like with missing values, the most conservative option is to keep outliers in your dataset. Keeping outliers is usually the better option when you’re not sure if they are errors.

With a large sample, outliers are expected and more likely to occur. But each outlier has less of an effect on your results when your sample is large enough. The central tendency and variability of your data won’t be as affected by a couple of extreme values when you have a large number of values.

If you have a small dataset, you may also want to retain as much data as possible to make sure you have enough statistical power . If your dataset ends up containing many outliers, you may need to use a statistical test that’s more robust to them. Non-parametric statistical tests perform better for these data.

Remove outliers

Outlier removal means deleting extreme values from your dataset before you perform statistical analyses . You aim to delete any dirty data while retaining true extreme values.

It’s a tricky procedure because it’s often impossible to tell the two types apart for sure. Deleting true outliers may lead to a biased dataset and an inaccurate conclusion.

For this reason, you should only remove outliers if you have legitimate reasons for doing so. It’s important to document each outlier you remove and your reasons so that other researchers can follow your procedures.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Statistical power
• Pearson correlation
• Degrees of freedom
• Statistical significance

Methodology

• Cluster sampling
• Stratified sampling
• Focus group
• Systematic review
• Ethnography
• Double-Barreled Question

Research bias

• Implicit bias
• Publication bias
• Cognitive bias
• Placebo effect
• Pygmalion effect
• Hindsight bias
• Overconfidence bias

Outliers are extreme values that differ from most values in the dataset. You find outliers at the extreme ends of your dataset.

Outliers can have a big impact on your statistical analyses and skew the results of any hypothesis test if they are inaccurate.

These extreme values can impact your statistical power as well, making it hard to detect a true effect if there is one.

You can choose from four main ways to detect outliers :

• Sorting your values from low to high and checking minimum and maximum values
• Visualizing your data with a box plot and looking for outliers
• Using the interquartile range to create fences for your data
• Using statistical procedures to identify extreme values

It’s best to remove outliers only when you have a sound reason for doing so.

Some outliers represent natural variations in the population , and they should be left as is in your dataset. These are called true outliers.

Other outliers are problematic and should be removed because they represent measurement errors , data entry or processing errors, or poor sampling.

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