Ready to dive into the world where data-driven strategies and human intuition collide? Ever wondered how President George W. Bush's reaction to 9/11 ties into education methods? Or how the 100,000 Lives Campaign saved thousands by using statistical evidence? Join us as we explore the sweeping changes brought by the digital revolution and the rise of Super Crunchers, and how they're reshaping everything from education to healthcare, business to public policy.

Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart

In today's digital age, our choices—from movies to music—are increasingly influenced by recommendation systems that harness the power of data and collective behavior. Platforms like Netflix and Pandora customize suggestions based on your past preferences, often introducing you to content you might have never discovered on your own. 

This phenomenon, known as "collaborative filtering," taps into the "wisdom of crowds," a concept where group predictions often surpass individual guesses. For instance, in "Who Wants to Be a Millionaire," the audience's collective answer is typically more accurate than a single expert's advice.

In the realm of relationships, eHarmony uses data-driven techniques to match couples based on compatibility, employing statistical regression to predict relationship success. This approach, while groundbreaking, raises debates about whether similar or complementary personalities make better partners. The matching industry's secretive data practices contrast with academia's open research ethos, highlighting a tension between commercial and scientific standards.

Businesses are leveraging data mining to optimize operations and customer interactions. Retailers like Wal-Mart use predictive analytics to manage inventory efficiently, even preemptively stocking items like Pop-Tarts ahead of hurricanes. Similarly, Harrah's casinos analyze customer behavior to enhance the gambling experience, ensuring players leave satisfied despite losses.

The power of data mining extends to consumer advocacy, with services like Farecast using Super Crunching to predict airfare trends, empowering customers to make informed purchasing decisions. This consumer-oriented data crunching is a counterbalance to corporate strategies that exploit pricing and customer behavior insights.

Looking ahead, data mining suggests a future where both companies and governments might offer personalized insights to enhance consumer decision-making. While the potential for privacy invasions exists, the benefits of informed choices could redefine consumer-business dynamics. As data-driven predictions become more integral to daily life, understanding the balance between privacy and personalization will be crucial.

Is Data-Driven Decision Making the Secret Behind Netflix's Success and eHarmony's Perfect Matches?

In the world of business, data-driven decisions are becoming key, and leading companies like Capital One are embracing this by conducting randomized trials to create their own data.

The concept of randomized testing was introduced by Ronald Fisher in the 1920s and has since become the gold standard in proving medical treatments' efficacy. Capital One, a pioneer in this area, uses randomized trials extensively to test new products, advertising approaches, and contract terms. They run thousands of experiments annually to determine what works best, such as deciding whether "LIMITED TIME OFFER" or "2.9 Percent Introductory Rate!" printed on an envelope yields better results.

Randomized trials allow businesses to measure the "treatment effect," effectively isolating the impact of specific interventions on outcomes. For example, Capital One's computer algorithms analyze customer interactions to tailor responses and offers, transforming customer service calls into sales opportunities. Similarly, in South Africa, Credit Indemnity used randomized trials to optimize their loan offers and discovered that adding a photo or a pre-solicitation call significantly increased response rates.

Offermatica.com, founded by Matt and James Roche, showcases the power of Internet randomization. Their platform helps businesses test different web page designs in real-time to see which generates more engagement and sales. For instance, Monster.com used their services to test 128 different web page permutations, leading to millions of dollars in additional revenue.

Randomization isn't just for marketing; it extends to employee and customer relationship management. Continental Airlines used randomized trials to improve customer loyalty by apologizing for "transportation events" and offering compensation, resulting in increased customer spending.

Despite its proven benefits, not all businesses have adopted randomized testing. Companies like Wal-Mart still rely heavily on historical data. However, the simplicity and effectiveness of randomized trials make them a powerful tool for businesses to proactively create useful data and make informed decisions.

Randomized trials are also making headway in policy-making, with bipartisan support for demonstration projects that test different policies in various states. This approach ensures that decisions are based on solid evidence, benefiting both businesses and government policy development. Overall, randomized testing is reshaping how decisions are made, driving innovation and optimizing outcomes in various fields.

Capital One Leads the Way in Business Innovation with Randomized Trials

In 1966, Heather Ross, a bold MIT economics graduate student, embarked on a groundbreaking experiment funded by a $5 million government grant to test the Negative Income Tax (NIT). 

This policy aimed to provide a minimum income, regardless of earnings. Her findings revealed an unexpected surge in divorce rates among families receiving NIT, while debunking fears that it would discourage employment. This experiment was pivotal, sparking a wave of randomized public policy trials, transforming how U.S. lawmakers evaluated government programs.

Fast forward to 1993, economist Larry Katz faced skepticism while advocating for job-search assistance programs to save $2 billion in unemployment insurance. Through randomized welfare-to-work tests, he demonstrated that such assistance helped unemployed individuals find jobs quicker, saving the government money. Katz’s evidence-based approach won over Congress, showcasing the power of randomization in policymaking.

Randomized trials have become a global phenomenon, with developing countries leading the charge due to cost-effective testing. The Poverty Action Lab at MIT, led by Esther Duflo, has significantly contributed by partnering with NGOs to test development strategies. Duflo's efforts in India, such as using cameras to curb teacher absenteeism, highlight the transformative potential of randomized testing.

An outstanding example is Mexico's Progresa program, initiated by President Ernesto Zedillo. This conditional cash transfer program incentivized poor families to invest in their children's education and health. Randomized trials demonstrated Progresa's success in improving education and health outcomes, leading to its continuation and expansion. Progresa's model was pivotal in reshaping policy globally, proving the efficacy of conditional cash transfers.

These stories illustrate the profound impact of randomized trials on public policy, demonstrating their ability to provide clear, actionable insights. The simplicity and transparency of these trials have revolutionized decision-making, setting a new standard for evidence-based governance worldwide.

How Did Heather Ross and Larry Katz Revolutionize Public Policy with Randomized Trials?

In 1992, physicians Gordon Guyatt and David Sackett from McMaster University introduced the concept of "evidence-based medicine" (EBM), advocating for treatment decisions to be guided by statistical evidence. EBM, however, remains controversial, with debates about the extent to which statistical analysis should influence medical practices.

The historical struggle of Ignaz Semmelweis, who discovered the importance of hand-washing in reducing mortality but faced ridicule and resistance, underscores the difficulty of changing medical practices.

Don Berwick, a modern advocate of EBM, launched the "100,000 Lives Campaign" in 2004 to save lives by implementing statistically proven medical reforms. Berwick's initiatives, such as elevating hospital beds to prevent lung infections, were based on solid statistical evidence. His efforts resulted in over 122,000 hospital deaths being prevented within 18 months, demonstrating the power and speed of implementing evidence-based changes in healthcare.

Despite these successes, the medical community often resists EBM due to entrenched practices and skepticism about new studies. Physicians like Dr. Barron Lerner continue to perform traditional exams despite evidence showing their limited effectiveness. Furthermore, medical myths persist, and many doctors cling to outdated treatments due to an initial mistaken consensus.

The rise of the Internet has significantly enhanced the dissemination of medical research, allowing faster access to relevant statistical studies. Tools like Isabel, a diagnostic software, assist doctors in identifying potential diseases based on patient symptoms. Isabel's effectiveness, highlighted by real-world cases, showcases the potential of technology in improving diagnostic accuracy.

The future of EBM lies in integrating digital medical records and real-time data analysis to provide personalized and accurate medical care. This shift towards data-driven decision-making aims to improve both treatment and diagnosis, overcoming the resistance rooted in traditional medical practices. As EBM continues to evolve, it promises to transform the healthcare industry by prioritizing statistical evidence and improving patient outcomes.

Don Berwick's 100,000 Lives Campaign: Revolutionizing Healthcare with Evidence-Based Medicine

Who’s more accurate: Super Crunchers or traditional experts? This question has been explored for decades, and the results often favor Super Crunchers. Ted Ruger, a law professor at the University of Pennsylvania, was skeptical when he heard political scientists Andrew Martin and Kevin Quinn claim they could predict Supreme Court decisions using a few variables. 

So, they decided to test it with a competition during the Supreme Court’s 2002 term. The Super Crunching algorithm predicted 75% of the Court’s decisions correctly, compared to the legal experts' 59.1%.

Paul Meehl’s 1954 book "Clinical Versus Statistical Prediction" shook the psychology world by showing that simple statistical models often outperformed experts in various fields. This trend continues today. For instance, Chris Snijders from Eindhoven University tested his Super Crunching model against professional corporate buyers and found that the model outperformed even experienced managers.

Why are humans bad at making predictions? Cognitive biases and overconfidence distort our judgments. We give too much weight to unusual events and cling to mistaken beliefs. Super Crunchers, on the other hand, don't suffer from these issues. They unemotionally update predictions based on new data and provide confidence intervals, something experts often fail to do.

Combining expertise with Super Crunching can improve decision-making, but models typically outperform even aided experts. For example, in parole decisions, states increasingly rely on statistical predictions rather than human judgment, as humans tend to make poorer decisions when they override these predictions.

Humans still play a crucial role in hypothesizing and generating variables for statistical analysis. Aaron Fink’s hypothesis about circumcision reducing HIV risk, later confirmed by studies, exemplifies this. The future lies in a collaboration where human intuition generates hypotheses and Super Crunchers validate them, leading to more accurate and reliable decisions.

Can Super Crunchers Outpredict Even the Sharpest Minds?

The author's nephew Marty has a T-shirt that says, “There are 10 types of people in the world…” and if you’re wondering what those ten types are, you’re missing the binary joke. The back of the shirt reads, “Those that understand binary, and those that don’t.” This encapsulates the essence of the digital revolution we’re living in—everything is becoming binary, from our emails to electronic health records, real estate filings, and even consumer purchases.

For instance, back in 1989, there was a study to see if car dealers discriminated against women or minorities. They sent testers to bargain at car dealerships with a uniform script. The results were shocking—white women paid 40% more than white men, black men paid double, and black women paid three times more. This small study pushed the retail industry towards no-haggle purchasing.

Fast forward to the new millennium, and researchers have been crunching numbers in massive class-action litigation against major automotive lenders. They found that African-American borrowers were charged significantly higher markups on loans compared to white borrowers, thanks to detailed electronic records. These records, combined with public databases, allowed them to identify racial disparities in lending practices, leading to caps on loan markups.

The commercialization of digital data is booming. Companies like Acxiom and ChoicePoint aggregate vast amounts of information, turning it into valuable commodities. For instance, Acxiom manages consumer information on nearly every household in the U.S., using it to predict consumer behavior and segment lifestyles.

The rise of neural networks has also revolutionized predictions. These networks simulate the human brain’s learning processes, updating their responses based on new information. For example, researchers used a neural network to predict winners in greyhound races more accurately than human experts.

Hollywood is also embracing neural predictions. Epagogix, founded by lawyer Dick Copaken, uses neural networks to predict a movie’s gross revenue based on script characteristics. This method has proven highly accurate, predicting six out of nine movies' profitability before production even began. Studios are now using these predictions to greenlight projects and optimize scripts.

Despite the success of data-driven decision-making, some industries are slow to adopt these methods due to resistance and the complexity of certain decisions. For example, book publishing is seen as an art, resistant to formulaic predictions. However, statistical analyses have shown that certain title characteristics can predict bestseller success.

As we move forward, the ability to capture, merge, and store digital data will continue to grow, driving the Super Crunching revolution. This shift is both exciting and daunting, raising questions about privacy and the balance between data-driven decisions and human creativity.

Super Crunching Revolution: How Digital Data and Neural Networks Are Transforming Industries

In a second-grade classroom in Sarasota, Florida, teacher Sandra Kay Daniel leads her students through a reading lesson with a rhythm and precision that feels almost musical. But the real drama of the scene isn’t just in the classroom—it’s the moment George W. Bush, sitting with the children, learns America is under attack on 9/11. This classroom became a focal point in Michael Moore’s Fahrenheit 9/11, but beyond the political context, it also highlights a fierce educational debate about the method Ms. Daniel was using: Direct Instruction (DI).

DI, a method developed by Siegfried “Zig” Engelmann in the 1960s, is controversial yet highly effective. It involves teachers following a precise script, ensuring that students grasp bite-sized concepts quickly and accurately, with all students participating in unison. This method’s success is backed by substantial evidence, including the results from Project Follow Through, the largest education study ever conducted, which found that DI outperformed other teaching methods in reading, math, and even self-esteem.

However, DI’s rigid structure faces resistance from educators who feel it stifles creativity and reduces teachers to mere robots. Despite this, many instructors, like those at Arundel Elementary in Baltimore, have come to appreciate DI’s efficiency and the success it brings to students, particularly those from disadvantaged backgrounds.

Engelmann’s data-driven approach, while supported by extensive evidence, challenges the traditional, intuition-based methods of teaching. This tension mirrors broader societal changes where data and algorithms increasingly guide decisions in fields ranging from education to medicine, often at the expense of individual discretion and creativity.

As data-driven decision-making continues to spread, resistance is expected, but embracing the benefits of Super Crunching while remaining critical and informed may be the best way forward.

Did George W. Bush's 9/11 Classroom Visit Highlight a Revolutionary Teaching Method?

Once upon a time, during a hike with his daughter Anna, the author found himself marveling at her budding intuition and knack for statistics. At just eight, she was already applying the "Two Standard Deviation" rule to her life experiences. 

As they climbed the Sleeping Giant trail, Anna confidently estimated she'd hiked it six times, with a standard deviation of two, but then reconsidered her mean to eight after reflecting on the likelihood of her memories fitting within a 95% probability range. This playful exploration of stats and intuition highlights a new intelligence paradigm: using numbers to refine gut feelings.

In the world of sports, economist Justin Wolfers stumbled upon an anomaly while analyzing college basketball betting data. His discovery of a 6% discrepancy in point spreads suggested possible point shaving, illustrating the power of statistical intuition in uncovering truths that pure number-crunching might miss.

Meanwhile, the controversial remarks of Lawrence Summers, former Harvard president, on gender differences in intelligence variability sparked a firestorm. Summers suggested that men might have more variable IQs, potentially explaining gender disparities in elite science roles. His hypothesis, though contentious, exemplified the use of statistical reasoning to explore complex social issues.

In healthcare, economist Ben Polak faced frustration with medical professionals' handling of probabilities. During his wife's pregnancy, he sought clarity on the probability of Down syndrome, only to encounter a lack of statistical sophistication. Polak's quest underscores the importance of Bayesian updating in medical decision-making, where integrating multiple data points can yield more accurate predictions.

These anecdotes paint a picture of a future where intuition and data coexist, enhancing decision-making across fields. As we embrace statistical tools like the standard deviation and Bayes' theorem, we move towards a world where numbers and intuition blend seamlessly, guiding us to smarter choices. In this future, those who can navigate both realms will lead the way, leveraging data to inform and validate their instincts.

From Hiking Trails to Harvard: How Numbers and Intuition Shape Decision-Making

In the digital age, our media choices are increasingly guided by recommendation systems that utilize data and collective behavior to suggest content. This concept, known as collaborative filtering, uses the "wisdom of crowds" to provide accurate predictions. In relationships, data-driven techniques like those used by eHarmony match couples based on compatibility, though this raises debates about the nature of successful partnerships.

Businesses benefit from data mining to optimize operations and enhance customer experiences, as seen with retailers managing inventory and casinos improving customer satisfaction. Data mining also empowers consumers, helping them make informed decisions about purchases such as airfare.

Randomized trials, a method introduced by Ronald Fisher, are becoming essential in business and policy-making. They allow companies like Capital One to test strategies and improve customer interactions, and have proven effective in global policy experiments. Evidence-based medicine, inspired by statistical analysis, aims to improve healthcare decisions despite resistance from traditional practices.

Super Crunchers, statistical models, often outperform human experts in making predictions due to their unbiased nature. This trend is evident in fields like law, finance, and even healthcare, where statistical predictions are increasingly favored.

The rise of digital data has revolutionized various industries, from retail to entertainment, with neural networks and predictive algorithms enhancing decision-making. Despite resistance, the integration of data-driven methods is reshaping business practices and public policies.

In education, evidence-based methods like Direct Instruction have shown significant success but face resistance due to their structured nature. The broader societal shift towards data-driven decisions challenges traditional intuition-based approaches.

As we blend statistical tools with intuition, we enhance decision-making across fields. This balance of numbers and gut feelings is key to navigating the future, where informed choices lead to better outcomes. Those adept at integrating both realms will drive innovation and smarter decisions.

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Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart

Ian Ayres

9 Key ideas

25 MINS

4.4 (602)

Ready to dive into the world where data-driven strategies and human intuition collide?

Ever wondered how President George W. Bush's reaction to 9/11 ties into education methods?

Or how the 100,000 Lives Campaign saved thousands by using statistical evidence?

Join us as we explore the sweeping changes brought by the digital revolution and the rise of Super Crunchers, and how they're reshaping everything from education to healthcare, business to public policy.

Key Ideas

#1