The Moneyball Effect: How smart data is transforming criminal justice, healthcare, music, and even government spending

Curated on Posted on by Stefaan Verhulst

TED: “When Anne Milgram became the Attorney General of New Jersey in 2007, she was stunned to find out just how little data was available on who was being arrested, who was being charged, who was serving time in jails and prisons, and who was being released. It turns out that most big criminal justice agencies like my own didn’t track the things that matter,” she says in today’s talk, filmed at TED@BCG. “We didn’t share data, or use analytics, to make better decisions and reduce crime.”
Milgram’s idea for how to change this: “I wanted to moneyball criminal justice.”
Moneyball, of course, is the name of a 2011 movie starring Brad Pitt and the book it’s based on, written by Michael Lewis in 2003. The term refers to a practice adopted by the Oakland A’s general manager Billy Beane in 2002 — the organization began basing decisions not on star power or scout instinct, but on statistical analysis of measurable factors like on-base and slugging percentages. This worked exceptionally well. On a tiny budget, the Oakland A’s made it to the playoffs in 2002 and 2003, and — since then — nine other major league teams have hired sabermetric analysts to crunch these types of numbers.
Milgram is working hard to bring smart statistics to criminal justice. To hear the results she’s seen so far, watch this talk. And below, take a look at a few surprising sectors that are getting the moneyball treatment as well.

Moneyballing music. Last year, Forbes magazine profiled the firm Next Big Sound, a company using statistical analysis to predict how musicians will perform in the market. The idea is that — rather than relying on the instincts of A&R reps — past performance on Pandora, Spotify, Facebook, etc can be used to predict future potential. The article reads, “For example, the company has found that musicians who gain 20,000 to 50,000 Facebook fans in one month are four times more likely to eventually reach 1 million. With data like that, Next Big Sound promises to predict album sales within 20% accuracy for 85% of artists, giving labels a clearer idea of return on investment.”
Moneyballing human resources. In November, The Atlantic took a look at the practice of “people analytics” and how it’s affecting employers. (Billy Beane had something to do with this idea — in 2012, he gave a presentation at the TLNT Transform Conference called “The Moneyball Approach to Talent Management.”) The article describes how Bloomberg reportedly logs its employees’ keystrokes and the casino, Harrah’s, tracks employee smiles. It also describes where this trend could be going — for example, how a video game called Wasabi Waiter could be used by employers to judge potential employees’ ability to take action, solve problems and follow through on projects. The article looks at the ways these types of practices are disconcerting, but also how they could level an inherently unequal playing field. After all, the article points out that gender, race, age and even height biases have been demonstrated again and again in our current hiring landscape.
Moneyballing healthcare. Many have wondered: what about a moneyball approach to medicine? (See this call out via Common Health, this piece in Wharton Magazine or this op-ed on The Huffington Post from the President of the New York State Health Foundation.) In his TED Talk, “What doctors can learn from each other,” Stefan Larsson proposed an idea that feels like something of an answer to this question. In the talk, Larsson gives a taste of what can happen when doctors and hospitals measure their outcomes and share this data with each other: they are able to see which techniques are proving the most effective for patients and make adjustments. (Watch the talk for a simple way surgeons can make hip surgery more effective.) He imagines a continuous learning process for doctors — that could transform the healthcare industry to give better outcomes while also reducing cost.
Moneyballing government. This summer, John Bridgeland (the director of the White House Domestic Policy Council under President George W. Bush) and Peter Orszag (the director of the Office of Management and Budget in Barack Obama’s first term) teamed up to pen a provocative piece for The Atlantic called, “Can government play moneyball?” In it, the two write, “Based on our rough calculations, less than $1 out of every $100 of government spending is backed by even the most basic evidence that the money is being spent wisely.” The two explain how, for example, there are 339 federally-funded programs for at-risk youth, the grand majority of which haven’t been evaluated for effectiveness. And while many of these programs might show great results, some that have been evaluated show troubling results. (For example, Scared Straight has been shown to increase criminal behavior.) Yet, some of these ineffective programs continue because a powerful politician champions them. While Bridgeland and Orszag show why Washington is so averse to making data-based appropriation decisions, the two also see the ship beginning to turn around. They applaud the Obama administration for a 2014 budget with an “unprecendented focus on evidence and results.” The pair also gave a nod to the nonprofit Results for America, which advocates that for every $99 spent on a program, $1 be spent on evaluating it. The pair even suggest a “Moneyball Index” to encourage politicians not to support programs that don’t show results.
In any industry, figuring out what to measure, how to measure it and how to apply the information gleaned from those measurements is a challenge. Which of the applications of statistical analysis has you the most excited? And which has you the most terrified?”

Selected Readings on Big Data

Curated on Posted on by Andrew Young

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of big data was originally published in 2014.

Big Data refers to the wide-scale collection, aggregation, storage, analysis and use of data. Government is increasingly in control of a massive amount of raw data that, when analyzed and put to use, can lead to new insights on everything from public opinion to environmental concerns. The burgeoning literature on Big Data argues that it generates value by: creating transparency; enabling experimentation to discover needs, expose variability, and improve performance; segmenting populations to customize actions; replacing/supporting human decision making with automated algorithms; and innovating new business models, products and services. The insights drawn from data analysis can also be visualized in a manner that passes along relevant information, even to those without the tech savvy to understand the data on its own terms (see The GovLab Selected Readings on Data Visualization).

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)

Australian Government Information Management Office. The Australian Public Service Big Data Strategy: Improved Understanding through Enhanced Data-analytics Capability Strategy Report. August 2013. http://bit.ly/17hs2xY.

  • This Big Data Strategy produced for Australian Government senior executives with responsibility for delivering services and developing policy is aimed at ingraining in government officials that the key to increasing the value of big data held by government is the effective use of analytics. Essentially, “the value of big data lies in [our] ability to extract insights and make better decisions.”
  • This positions big data as a national asset that can be used to “streamline service delivery, create opportunities for innovation, identify new service and policy approaches as well as supporting the effective delivery of existing programs across a broad range of government operations.”

Bollier, David. The Promise and Peril of Big Data. The Aspen Institute, Communications and Society Program, 2010. http://bit.ly/1a3hBIA.

  • This report captures insights from the 2009 Roundtable exploring uses of Big Data within a number of important consumer behavior and policy implication contexts.
  • The report concludes that, “Big Data presents many exciting opportunities to improve modern society. There are incalculable opportunities to make scientific research more productive, and to accelerate discovery and innovation. People can use new tools to help improve their health and well-being, and medical care can be made more efficient and effective. Government, too, has a great stake in using large databases to improve the delivery of government services and to monitor for threats to national security.
  • However, “Big Data also presents many formidable challenges to government and citizens precisely because data technologies are becoming so pervasive, intrusive and difficult to understand. How shall society protect itself against those who would misuse or abuse large databases? What new regulatory systems, private-law innovations or social practices will be capable of controlling anti-social behaviors–and how should we even define what is socially and legally acceptable when the practices enabled by Big Data are so novel and often arcane?”

Boyd, Danah and Kate Crawford. “Six Provocations for Big Data.” A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society. September 2011. http://bit.ly/1jJstmz.

  • In this paper, Boyd and Crawford raise challenges to unchecked assumptions and biases regarding big data. The paper makes a number of assertions about the “computational culture” of big data and pushes back against those who consider big data to be a panacea.
  • The authors’ provocations for big data are:
    • Automating Research Changes the Definition of Knowledge
    • Claims to Objectivity and Accuracy are Misleading
    • Big Data is not always Better Data
    • Not all Data is Equivalent
    • Just Because it is accessible doesn’t make it ethical
    • Limited Access to Big Data creates New Digital Divide

The Economist Intelligence Unit. Big Data and the Democratisation of Decisions. October 2012. http://bit.ly/17MpH8L.

  • This report from the Economist Intelligence Unit focuses on the positive impact of big data adoption in the private sector, but its insights can also be applied to the use of big data in governance.
  • The report argues that innovation can be spurred by democratizing access to data, allowing a diversity of stakeholders to “tap data, draw lessons and make business decisions,” which in turn helps companies and institutions respond to new trends and intelligence at varying levels of decision-making power.

Manyika, James, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, and Angela Hung Byers. Big Data: The Next Frontier for Innovation, Competition, and Productivity.  McKinsey & Company. May 2011. http://bit.ly/18Q5CSl.

  • This report argues that big data “will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, and that “leaders in every sector will have to grapple with the implications of big data.” 
  • The report offers five broad ways in which using big data can create value:
    • First, big data can unlock significant value by making information transparent and usable at much higher frequency.
    • Second, as organizations create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days, and therefore expose variability and boost performance.
    • Third, big data allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services.
    • Fourth, big sophisticated analytics can substantially improve decision-making.
    • Finally, big data can be used to improve the development of the next generation of products and services.

The Partnership for Public Service and the IBM Center for The Business of Government. “From Data to Decisions II: Building an Analytics Culture.” October 17, 2012. https://bit.ly/2EbBTMg.

  • This report discusses strategies for better leveraging data analysis to aid decision-making. The authors argue that, “Organizations that are successful at launching or expanding analytics program…systematically examine their processes and activities to ensure that everything they do clearly connects to what they set out to achieve, and they use that examination to pinpoint weaknesses or areas for improvement.”
  • While the report features many strategies for government decisions-makers, the central recommendation is that, “leaders incorporate analytics as a way of doing business, making data-driven decisions transparent and a fundamental approach to day-to-day management. When an analytics culture is built openly, and the lessons are applied routinely and shared widely, an agency can embed valuable management practices in its DNA, to the mutual benet of the agency and the public it serves.”

TechAmerica Foundation’s Federal Big Data Commission. “Demystifying Big Data: A Practical Guide to Transforming the Business of Government.” 2013. http://bit.ly/1aalUrs.

  • This report presents key big data imperatives that government agencies must address, the challenges and the opportunities posed by the growing volume of data and the value Big Data can provide. The discussion touches on the value of big data to businesses and organizational mission, presents case study examples of big data applications, technical underpinnings and public policy applications.
  • The authors argue that new digital information, “effectively captured, managed and analyzed, has the power to change every industry including cyber security, healthcare, transportation, education, and the sciences.” To ensure that this opportunity is realized, the report proposes a detailed big data strategy framework with the following steps: define, assess, plan, execute and review.

World Economic Forum. “Big Data, Big Impact: New Possibilities for International Development.” 2012. http://bit.ly/17hrTKW.

  • This report examines the potential for channeling the “flood of data created every day by the interactions of billions of people using computers, GPS devices, cell phones, and medical devices” into “actionable information that can be used to identify needs, provide services, and predict and prevent crises for the benefit of low-income populations”
  • The report argues that, “To realise the mutual benefits of creating an environment for sharing mobile-generated data, all ecosystem actors must commit to active and open participation. Governments can take the lead in setting policy and legal frameworks that protect individuals and require contractors to make their data public. Development organisations can continue supporting governments and demonstrating both the public good and the business value that data philanthropy can deliver. And the private sector can move faster to create mechanisms for the sharing data that can benefit the public.”

How Government Can Make Open Data Work

Curated on Posted on by Stefaan Verhulst

Joel Gurin in Information Week: “At the GovLab at New York University, where I am senior adviser, we’re taking a different approach than McKinsey’s to understand the evolving value of government open data: We’re studying open data companies from the ground up. I’m now leading the GovLab’s Open Data 500 project, funded by the John S. and James L. Knight Foundation, to identify and examine 500 American companies that use government open data as a key business resource.
Our preliminary results show that government open data is fueling companies both large and small, across the country, and in many sectors of the economy, including health, finance, education, energy, and more. But it’s not always easy to use this resource. Companies that use government open data tell us it is often incomplete, inaccurate, or trapped in hard-to-use systems and formats.
It will take a thorough and extended effort to make government data truly useful. Based on what we are hearing and the research I did for my book, here are some of the most important steps the federal government can take, starting now, to make it easier for companies to add economic value to the government’s data.
1. Improve data quality
The Open Data Policy not only directs federal agencies to release more open data; it also requires them to release information about data quality. Agencies will have to begin improving the quality of their data simply to avoid public embarrassment. We can hope and expect that they will do some data cleanup themselves, demand better data from the businesses they regulate, or use creative solutions like turning to crowdsourcing for help, as USAID did to improve geospatial data on its grantees.
 
 

2. Keep improving open data resources
The government has steadily made Data.gov, the central repository of federal open data, more accessible and useful, including a significant relaunch last week. To the agency’s credit, the GSA, which administers Data.gov, plans to keep working to make this key website still better. As part of implementing the Open Data Policy, the administration has also set up Project Open Data on GitHub, the world’s largest community for open-source software. These resources will be helpful for anyone working with open data either inside or outside of government. They need to be maintained and continually improved.
3. Pass DATA
The Digital Accountability and Transparency Act would bring transparency to federal government spending at an unprecedented level of detail. The Act has strong bipartisan support. It passed the House with only one dissenting vote and was unanimously approved by a Senate committee, but still needs full Senate approval and the President’s signature to become law. DATA is also supported by technology companies who see it as a source of new open data they can use in their businesses. Congress should move forward and pass DATA as the logical next step in the work that the Obama administration’s Open Data Policy has begun.
4. Reform the Freedom of Information Act
Since it was passed in 1966, the federal Freedom of Information Act has gone through two major revisions, both of which strengthened citizens’ ability to access many kinds of government data. It’s time for another step forward. Current legislative proposals would establish a centralized web portal for all federal FOIA requests, strengthen the FOIA ombudsman’s office, and require agencies to post more high-interest information online before they receive formal requests for it. These changes could make more information from FOIA requests available as open data.
5. Engage stakeholders in a genuine way
Up to now, the government’s release of open data has largely been a one-way affair: Agencies publish datasets that they hope will be useful without consulting the organizations and companies that want to use it. Other countries, including the UK, France, and Mexico, are building in feedback loops from data users to government data providers, and the US should, too. The Open Data Policy calls for agencies to establish points of contact for public feedback. At the GovLab, we hope that the Open Data 500 will help move that process forward. Our research will provide a basis for new, productive dialogue between government agencies and the businesses that rely on them.
6. Keep using federal challenges to encourage innovation
The federal Challenge.gov website applies the best principles of crowdsourcing and collective intelligence. Agencies should use this approach extensively, and should pose challenges using the government’s open data resources to solve business, social, or scientific problems. Other approaches to citizen engagement, including federally sponsored hackathons and the White House Champions of Change program, can play a similar role.
Through the Open Data Policy and other initiatives, the Obama administration has set the right goals. Now it’s time to implement and move toward what US CTO Todd Park calls “data liberation.” Thousands of companies, organizations, and individuals will benefit.”

GSA’s Challenge.gov Earns Harvard Innovation Award

Curated on Posted on by Stefaan Verhulst

Press Release: “The Ash Center for Democratic Governance and Innovation at the John F. Kennedy School of Government at Harvard University today announced the U.S. General Services Administration’s (GSA) Challenge.gov as a winner of the 2013 Innovations in American Government Award from a pool of more than 600 applicants.
GSA launched Challenge.gov in July 2010 in response to an Obama Administration memo tasking the agency with building a platform that allowed entrepreneurs, innovators, and the public to compete for prestige and prizes by providing the government with novel solutions to tough problems. Challenge.gov was developed in partnership with New York City-based ChallengePost, the leading platform for software competitions and hackathons. Since its launch, Challenge.gov has been used by 59 federal agencies to crowd source solutions and has received 3.5 million visits from 220 countries and territories and more than 11,000 U.S. cities. Challenge.gov has conducted nearly 300 scientific, engineering, design, multimedia, ideation, and software challenges, resulting in unprecedented public-private partnerships….
Examples of Challenge.gov competitions include a Robocall Challenge that has blocked 84,000 computer driven advertising phone calls so far, a Disability Employment Apps Challenge that sought innovative technology tools to improve employment opportunities and outcomes for people with disabilities, and the Blue Button for All Americans Contest that helps veterans have access to their health information.
Established in 1985 at Harvard University by the Ford Foundation, the Innovations in American Government Award Program has honored nearly 200 federal, state, local, and tribal government agencies. The Innovations Award Program provides concrete evidence that government can work to improve the quality of life of citizens. Many award-winning programs have been replicated across jurisdictions and policy areas, and some have served as harbingers of today’s reform strategies or as forerunners to state and federal legislation. By highlighting exemplary models of government’s innovative programs for more than 20 years, the Innovations Award Program drives continued progress and encourages research and teaching cases at Harvard University and other academic institutions worldwide. Nominations for the next Innovations in American Government Awards competition may be submitted at www.innovationsaward.harvard.edu.”

Citizen roles in civic problem-solving and innovation

Curated on Posted on by Stefaan Verhulst

Satish Nambisan: “Can citizens be fruitfully engaged in solving civic problems? Recent initiatives in cities such as Boston (Citizens Connect), Chicago (Smart Chicago Collaborative), San Francisco (ImproveSF) and New York (NYC BigApps) indicate that citizens can be involved in not just identifying and reporting civic problems but in conceptualizing, designing and developing, and implementing solutions as well.
The availability of new technologies (e.g. social media) has radically lowered the cost of collaboration and the “distance” between government agencies and the citizens they serve. Further involving citizens — who are often closest to and possess unique knowledge about the problems they face — makes a lot of sense given the increasing complexity of the problems that need to be addressed.
A recent research report that I wrote highlights four distinct roles that citizens can play in civic innovation and problem-solving.
As explorer, citizens can identify and report emerging and existing civic problems. For example, Boston’s Citizen Connect initiative enables citizens to use specially built smartphone apps to report minor and major civic problems (from potholes and graffiti to water/air pollution). Closer to home, both Wisconsin and Minnesota have engaged thousands of citizen volunteers in collecting data on the quality of water in their neighborhood streams, lakes and rivers (the data thus gathered are analyzed by the state pollution control agency). Citizens also can be engaged in data analysis. The N.Y.-based Datakind initiative involves citizen volunteers using their data analysis skills to mine public data in health, education, environment, etc., to identify important civic issues and problems.
As “ideator,”citizens can conceptualize novel solutions to well-defined problems in public services. For example, the federal government’s Challenge.gov initiative employs online contests and competitions to solicit innovative ideas from citizens to solve important civic problems. Such “crowdsourcing” initiatives also have been launched at the county, city and state levels (e.g. Prize2theFuture competition in Birmingham, Ala.; ImproveSF in San Francisco).
As designer, citizens can design and/or develop implementable solutions to well-defined civic problems. For example, as part of initiatives such as NYC Big Apps and Apps for California, citizens have designed mobile apps to address specific issues such as public parking availability, public transport delays, etc. Similarly, the City Repair project in Portland, Ore., focuses on engaging citizens in co-designing and creatively transforming public places into sustainable community-oriented urban spaces.
As diffuser,citizens can play the role of a change agent and directly support the widespread adoption of civic innovations and solutions. For example, in recent years, physicians interacting with peer physicians in dedicated online communities have assisted federal and state government agencies in diffusing health technology innovations such as electronic medical record systems (EMRs).
In the private sector, companies across industries have benefited much from engaging with their customers in innovation. Evidence so far suggests that the benefits from citizen engagement in civic problem-solving are equally tangible, valuable and varied. However, the challenges associated with organizing such citizen co-creation initiatives are also many and imply the need for government agencies to adopt an intentional, well-thought-out approach….”

How Internet surveillance predicts disease outbreak before WHO

Curated on Posted on by Stefaan Verhulst

Kurzweil News: “Have you ever Googled for an online diagnosis before visiting a doctor? If so, you may have helped provide early warning of an infectious disease epidemic.
In a new study published in Lancet Infectious Diseases, Internet-based surveillance has been found to detect infectious diseases such as Dengue Fever and Influenza up to two weeks earlier than traditional surveillance methods, according to Queensland University of Technology (QUT) research fellow and senior author of the paper Wenbiao Hu.
Hu, based at the Institute for Health and Biomedical Innovation, said there was often a lag time of two weeks before traditional surveillance methods could detect an emerging infectious disease.
“This is because traditional surveillance relies on the patient recognizing the symptoms and seeking treatment before diagnosis, along with the time taken for health professionals to alert authorities through their health networks. In contrast, digital surveillance can provide real-time detection of epidemics.”
Hu said the study used search engine algorithms such as Google Trends and Google Insights. It found that detecting the 2005–06 avian influenza outbreak “Bird Flu” would have been possible between one and two weeks earlier than official surveillance reports.
“In another example, a digital data collection network was found to be able to detect the SARS outbreak more than two months before the first publications by the World Health Organization (WHO),” Hu said.
According to this week’s CDC FluView report published Jan. 17, 2014, influenza activity in the United States remains high overall, with 3,745 laboratory-confirmed influenza-associated hospitalizations reported since October 1, 2013 (credit: CDC)
“Early detection means early warning and that can help reduce or contain an epidemic, as well alert public health authorities to ensure risk management strategies such as the provision of adequate medication are implemented.”
Hu said the study found that social media including Twitter and Facebook and microblogs could also be effective in detecting disease outbreaks. “The next step would be to combine the approaches currently available such as social media, aggregator websites, and search engines, along with other factors such as climate and temperature, and develop a real-time infectious disease predictor.”
“The international nature of emerging infectious diseases combined with the globalization of travel and trade, have increased the interconnectedness of all countries and that means detecting, monitoring and controlling these diseases is a global concern.”
The other authors of the paper were Gabriel Milinovich (first author), Gail Williams and Archie Clements from the University of Queensland School of Population, Health and State.
Supramap 
Another powerful tool is Supramap, a web application that synthesizes large, diverse datasets so that researchers can better understand the spread of infectious diseases across hosts and geography by integrating genetic, evolutionary, geospatial, and temporal data. It is now open-source — create your own maps here.
Associate Professor Daniel Janies, Ph.D., an expert in computational genomics at the Wexner Medical Center at The Ohio State University (OSU), worked with software engineers at the Ohio Supercomputer Center (OSC) to allow researchers and public safety officials to develop other front-end applications that draw on the logic and computing resources of Supramap.
It was originally developed in 2007 to track the spread and evolution of pandemic (H1N1) and avian influenza (H5N1).
“Using SUPRAMAP, we initially developed maps that illustrated the spread of drug-resistant influenza and host shifts in H1N1 and H5N1 influenza and in coronaviruses, such as SARS,” said Janies. “SUPRAMAP allows the user to track strains carrying key mutations in a geospatial browser such as Google Earth. Our software allows public health scientists to update and view maps on the evolution and spread of pathogens.”
Grant funding through the U.S. Army Research Laboratory and Office supports this Innovation Group on Global Infectious Disease Research project. Support for the computational requirements of the project comes from  the American Museum of Natural History (AMNH) and OSC. Ohio State’s Wexner Medical Center, Department of Biomedical Informatics and offices of Academic Affairs and Research provide additional support.”
See also

Safety Datapalooza Shows Power of Data.gov Communities

Curated on Posted on by Stefaan Verhulst

Lisa Nelson at DigitalGov: “The White House Office of Public Engagement held the first Safety Datapalooza illustrating the power of Data.gov communities. Federal Chief Technology Officer Todd Park and Deputy Secretary of Transportation John Porcari hosted the event, which touted the data available on Safety.Data.gov and the community of innovators using it to make effective tools for consumers.
The event showcased many of the  tools that have been produced as a result of  opening this safety data including:

  • PulsePoint, from the San Ramon Fire Protection District, a lifesaving mobile app that allows CPR-trained volunteers to be notified if someone nearby is in need of emergency assistance;
  • Commute and crime maps, from Trulia, allow home buyers to choose their new residence based on two important everyday factors; and
  • Hurricane App, from the American Red Cross, to monitor storm conditions, prepare your family and home, find help, and let others know you’re safe even if the power is out;

Safety data is far from alone in generating innovative ideas and gathering a community of developers and entrepreneurs, Data.gov currently has 16 different topically diverse communities on land and sea — the Cities and Oceans communities being two such examples. Data.gov’s communities are a virtual meeting spot for interested parties across government, academia and industry to come together and put the data to use. Data.gov enables a whole set of tools to make these communities come to life: apps, blogs, challenges, forums, ranking, rating and wikis.
For a summary of the Safety Datapalooza visit Transportation’s “Fast Lane” blog.”

EPA Launches New Citizen Science Website

Curated on Posted on by Stefaan Verhulst

Press Release: “The U.S. Environmental Protection Agency has revamped its Citizen Science website to provide new resources and success stories to assist the public in conducting scientific research and collecting data to better understand their local environment and address issues of concern. The website can be found at www.epa.gov/region2/citizenscience.
“Citizen Science is an increasingly important part of EPA’s commitment to using sound science and technology to protect people’s health and safeguard the environment,” said Judith A. Enck, EPA Regional Administrator. “The EPA encourages the public to use the new website as a tool in furthering their scientific investigations and developing solutions to pollution problems.”
The updated website now offers detailed information about air, water and soil monitoring, including recommended types of equipment and resources for conducting investigations. It also includes case studies and videotapes that showcase successful citizen science projects in New York and New Jersey, provides funding opportunities, quality assurance information and workshops and webinars.”

Garbage In, Garbage Out… Or, How to Lie with Bad Data

Curated on Posted on by Stefaan Verhulst
Medium: For everyone who slept through Stats 101, Charles Wheelan’s Naked Statistics is a lifesaver. From batting averages and political polls to Schlitz ads and medical research, Wheelan “illustrates exactly why even the most reluctant mathophobe is well advised to achieve a personal understanding of the statistical underpinnings of life” (New York Times). What follows is adapted from the book, out now in paperback.
Behind every important study there are good data that made the analysis possible. And behind every bad study . . . well, read on. People often speak about “lying with statistics.” I would argue that some of the most egregious statistical mistakes involve lying with data; the statistical analysis is fine, but the data on which the calculations are performed are bogus or inappropriate. Here are some common examples of “garbage in, garbage out.”

Selection Bias

….Selection bias can be introduced in many other ways. A survey of consumers in an airport is going to be biased by the fact that people who fly are likely to be wealthier than the general public; a survey at a rest stop on Interstate 90 may have the opposite problem. Both surveys are likely to be biased by the fact that people who are willing to answer a survey in a public place are different from people who would prefer not to be bothered. If you ask 100 people in a public place to complete a short survey, and 60 are willing to answer your questions, those 60 are likely to be different in significant ways from the 40 who walked by without making eye contact.

Publication Bias

Positive findings are more likely to be published than negative findings, which can skew the results that we see. Suppose you have just conducted a rigorous, longitudinal study in which you find conclusively that playing video games does not prevent colon cancer. You’ve followed a representative sample of 100,000 Americans for twenty years; those participants who spend hours playing video games have roughly the same incidence of colon cancer as the participants who do not play video games at all. We’ll assume your methodology is impeccable. Which prestigious medical journal is going to publish your results?

Most things don’t prevent cancer.

None, for two reasons. First, there is no strong scientific reason to believe that playing video games has any impact on colon cancer, so it is not obvious why you were doing this study. Second, and more relevant here, the fact that something does not prevent cancer is not a particularly interesting finding. After all, most things don’t prevent cancer. Negative findings are not especially sexy, in medicine or elsewhere.
The net effect is to distort the research that we see, or do not see. Suppose that one of your graduate school classmates has conducted a different longitudinal study. She finds that people who spend a lot of time playing video games do have a lower incidence of colon cancer. Now that is interesting! That is exactly the kind of finding that would catch the attention of a medical journal, the popular press, bloggers, and video game makers (who would slap labels on their products extolling the health benefits of their products). It wouldn’t be long before Tiger Moms all over the country were “protecting” their children from cancer by snatching books out of their hands and forcing them to play video games instead.
Of course, one important recurring idea in statistics is that unusual things happen every once in a while, just as a matter of chance. If you conduct 100 studies, one of them is likely to turn up results that are pure nonsense—like a statistical association between playing video games and a lower incidence of colon cancer. Here is the problem: The 99 studies that find no link between video games and colon cancer will not get published, because they are not very interesting. The one study that does find a statistical link will make it into print and get loads of follow-on attention. The source of the bias stems not from the studies themselves but from the skewed information that actually reaches the public. Someone reading the scientific literature on video games and cancer would find only a single study, and that single study will suggest that playing video games can prevent cancer. In fact, 99 studies out of 100 would have found no such link.

Recall Bias

Memory is a fascinating thing—though not always a great source of good data. We have a natural human impulse to understand the present as a logical consequence of things that happened in the past—cause and effect. The problem is that our memories turn out to be “systematically fragile” when we are trying to explain some particularly good or bad outcome in the present. Consider a study looking at the relationship between diet and cancer. In 1993, a Harvard researcher compiled a data set comprising a group of women with breast cancer and an age-matched group of women who had not been diagnosed with cancer. Women in both groups were asked about their dietary habits earlier in life. The study produced clear results: The women with breast cancer were significantly more likely to have had diets that were high in fat when they were younger.
Ah, but this wasn’t actually a study of how diet affects the likelihood of getting cancer. This was a study of how getting cancer affects a woman’s memory of her diet earlier in life. All of the women in the study had completed a dietary survey years earlier, before any of them had been diagnosed with cancer. The striking finding was that women with breast cancer recalled a diet that was much higher in fat than what they actually consumed; the women with no cancer did not.

Women with breast cancer recalled a diet that was much higher in fat than what they actually consumed; the women with no cancer did not.

The New York Times Magazine described the insidious nature of this recall bias:

The diagnosis of breast cancer had not just changed a woman’s present and the future; it had altered her past. Women with breast cancer had (unconsciously) decided that a higher-fat diet was a likely predisposition for their disease and (unconsciously) recalled a high-fat diet. It was a pattern poignantly familiar to anyone who knows the history of this stigmatized illness: these women, like thousands of women before them, had searched their own memories for a cause and then summoned that cause into memory.

Recall bias is one reason that longitudinal studies are often preferred to cross-sectional studies. In a longitudinal study the data are collected contemporaneously. At age five, a participant can be asked about his attitudes toward school. Then, thirteen years later, we can revisit that same participant and determine whether he has dropped out of high school. In a cross-sectional study, in which all the data are collected at one point in time, we must ask an eighteen-year-old high school dropout how he or she felt about school at age five, which is inherently less reliable.

Survivorship Bias

Suppose a high school principal reports that test scores for a particular cohort of students has risen steadily for four years. The sophomore scores for this class were better than their freshman scores. The scores from junior year were better still, and the senior year scores were best of all. We’ll stipulate that there is no cheating going on, and not even any creative use of descriptive statistics. Every year this cohort of students has done better than it did the preceding year, by every possible measure: mean, median, percentage of students at grade level, and so on. Would you (a) nominate this school leader for “principal of the year” or (b) demand more data?

If you have a room of people with varying heights, forcing the short people to leave will raise the average height in the room, but it doesn’t make anyone taller.

I say “b.” I smell survivorship bias, which occurs when some or many of the observations are falling out of the sample, changing the composition of the observations that are left and therefore affecting the results of any analysis. Let’s suppose that our principal is truly awful. The students in his school are learning nothing; each year half of them drop out. Well, that could do very nice things for the school’s test scores—without any individual student testing better. If we make the reasonable assumption that the worst students (with the lowest test scores) are the most likely to drop out, then the average test scores of those students left behind will go up steadily as more and more students drop out. (If you have a room of people with varying heights, forcing the short people to leave will raise the average height in the room, but it doesn’t make anyone taller.)

Healthy User Bias

People who take vitamins regularly are likely to be healthy—because they are the kind of people who take vitamins regularly! Whether the vitamins have any impact is a separate issue. Consider the following thought experiment. Suppose public health officials promulgate a theory that all new parents should put their children to bed only in purple pajamas, because that helps stimulate brain development. Twenty years later, longitudinal research confirms that having worn purple pajamas as a child does have an overwhelmingly large positive association with success in life. We find, for example, that 98 percent of entering Harvard freshmen wore purple pajamas as children (and many still do) compared with only 3 percent of inmates in the Massachusetts state prison system.

The purple pajamas do not matter.

Of course, the purple pajamas do not matter; but having the kind of parents who put their children in purple pajamas does matter. Even when we try to control for factors like parental education, we are still going to be left with unobservable differences between those parents who obsess about putting their children in purple pajamas and those who don’t. As New York Times health writer Gary Taubes explains, “At its simplest, the problem is that people who faithfully engage in activities that are good for them—taking a drug as prescribed, for instance, or eating what they believe is a healthy diet—are fundamentally different from those who don’t.” This effect can potentially confound any study trying to evaluate the real effect of activities perceived to be healthful, such as exercising regularly or eating kale. We think we are comparing the health effects of two diets: kale versus no kale. In fact, if the treatment and control groups are not randomly assigned, we are comparing two diets that are being eaten by two different kinds of people. We have a treatment group that is different from the control group in two respects, rather than just one.

If statistics is detective work, then the data are the clues. My wife spent a year teaching high school students in rural New Hampshire. One of her students was arrested for breaking into a hardware store and stealing some tools. The police were able to crack the case because (1) it had just snowed and there were tracks in the snow leading from the hardware store to the student’s home; and (2) the stolen tools were found inside. Good clues help.
Like good data. But first you have to get good data, and that is a lot harder than it seems.

From funding agencies to scientific agency –

Curated on Posted on by Stefaan Verhulst

New paper on “Collective allocation of science funding as an alternative to peer review”: “Publicly funded research involves the distribution of a considerable amount of money. Funding agencies such as the US National Science Foundation (NSF), the US National Institutes of Health (NIH) and the European Research Council (ERC) give billions of dollars or euros of taxpayers’ money to individual researchers, research teams, universities, and research institutes each year. Taxpayers accordingly expect that governments and funding agencies will spend their money prudently and efficiently.

Investing money to the greatest effect is not a challenge unique to research funding agencies and there are many strategies and schemes to choose from. Nevertheless, most funders rely on a tried and tested method in line with the tradition of the scientific community: the peer review of individual proposals to identify the most promising projects for funding. This method has been considered the gold standard for assessing the scientific value of research projects essentially since the end of the Second World War.

However, there is mounting critique of the use of peer review to direct research funding. High on the list of complaints is the cost, both in terms of time and money. In 2012, for example, NSF convened more than 17,000 scientists to review 53,556 proposals [1]. Reviewers generally spend a considerable time and effort to assess and rate proposals of which only a minority can eventually get funded. Of course, such a high rejection rate is also frustrating for the applicants. Scientists spend an increasing amount of time writing and submitting grant proposals. Overall, the scientific community invests an extraordinary amount of time, energy, and effort into the writing and reviewing of research proposals, most of which end up not getting funded at all. This time would be better invested in conducting the research in the first place.

Peer review may also be subject to biases, inconsistencies, and oversights. The need for review panels to reach consensus may lead to sub‐optimal decisions owing to the inherently stochastic nature of the peer review process. Moreover, in a period where the money available to fund research is shrinking, reviewers may tend to “play it safe” and select proposals that have a high chance of producing results, rather than more challenging and ambitious projects. Additionally, the structuring of funding around calls‐for‐proposals to address specific topics might inhibit serendipitous discovery, as scientists work on problems for which funding happens to be available rather than trying to solve more challenging problems.

The scientific community holds peer review in high regard, but it may not actually be the best possible system for identifying and supporting promising science. Many proposals have been made to reform funding systems, ranging from incremental changes to peer review—including careful selection of reviewers [2] and post‐hoc normalization of reviews [3]—to more radical proposals such as opening up review to the entire online population [4] or removing human reviewers altogether by allocating funds through an objective performance measure [5].

We would like to add another alternative inspired by the mathematical models used to search the internet for relevant information: a highly decentralized funding model in which the wisdom of the entire scientific community is leveraged to determine a fair distribution of funding. It would still require human insight and decision‐making, but it would drastically reduce the overhead costs and may alleviate many of the issues and inefficiencies of the proposal submission and peer review system, such as bias, “playing it safe”, or reluctance to support curiosity‐driven research.

Our proposed system would require funding agencies to give all scientists within their remit an unconditional, equal amount of money each year. However, each scientist would then be required to pass on a fixed percentage of their previous year’s funding to other scientists whom they think would make best use of the money (Fig 1). Every year, then, scientists would receive a fixed basic grant from their funding agency combined with an elective amount of funding donated by their peers. As a result of each scientist having to distribute a given percentage of their previous year’s budget to other scientists, money would flow through the scientific community. Scientists who are generally anticipated to make the best use of funding will accumulate more.”