Reporting from the National Science Writers Meeting in Columbus, Ohio

As someone who’s still learning the ropes, I was excited to attend my first science writers meeting in Columbus this weekend. The National Association of Science Writers (NASW) and Council for the Advancement of Science Writing (CASW) organized the meeting, which included a nice variety of professional development workshops, briefings on the latest scientific research, and some field trips. It included a couple parties at a nearby brewery too, so I knew I was in good company, and I was happy to make some new friends and contacts. Here’s my name tag, which was a convenient little book of the program (and you can guess who I wrote down as my science hero):

photo 1

I’ll give you some highlights of a couple sessions that interested me. People “live-tweeted” most of the sessions too at #sciwri14.

NASW Meeting

One of the most useful sessions for me was the “pitch slam,” where writers had a single minute to pitch a story idea to editors, who gave feedback in real time. (The editors came from Slate, NPR, Popular Science, Discover, NOVA, Scientific American, and New York Times.) Speaking in front of the microphone understandably made people nervous, but I think I heard some pretty good pitches. Since I’m trained as a scientist, my approach to a science story or issue is to keep asking questions, but it sounds like editors want answers too! It’s important to be concise and clearly state at the beginning what the narrative thrust is and why the story is interesting. One should also describe the implications of the scientific result are why they’re surprising or new. Science stories need characters too, but that can come afterward. And one should keep in mind the audience of readers who would most likely read it, since some stories are more appropriate in particular news outlets rather than others. For example, Popular Science usually publishes “forward-looking” stories, so they’d be less interested in pieces focused on historical scientific advances.

The session on “diving into controversy and politics” was popular too, and it included Coral Davenport (New York Times), David Malakoff (Science Insider), and Nancy Shute (NPR). They spoke about hot-button topics in the news today—mainly climate change and Ebola. Davenport argued that climate change (along with energy and environment policy) is now a top-tier election issue and that this is mainly due to President Obama’s Environmental Protection Agency (EPA) regulations for coal-fired power plants, Tom Steyer’s money, and current weather events. She made a fairly convincing argument, but I think she overstated how new this development is, as fracking and the Keystone XL pipeline have been polarizing issues well before this midterm election campaign. Malakoff spoke about related topics and suggested that one should never pitch a “science policy” story (that is, one should frame the story differently). He pointed out that some stories are about a disagreement while others are about setting priorities. It’s important to state as clearly as possible who believes what and what their agenda is. We should ask whether the data and scientific results lead us to a particular policy prescription, and we should distinguish between scientists’ research and their opinions about which policy to advocate. We should write about the effects and impacts of particular policies, and then the reader can make his/her own decision.

The awards night took place on Saturday, and I was inspired to see so many excellent award-winning science writers. The winners included Azeen Ghorayshi for the Clark/Payne Award, Elisabeth Rosenthal for the Cohn Prize in medical science reporting, and the following Science in Society Journalism Award winners: Sheri Fink, Amy Harmon, Phil McKenna, Cally Carswell, and Charles Seife.

CASW Meeting

Getting back to climate change, on Sunday we toured the impressive Byrd Polar Research Center of Ohio State University. Lonnie Thompson and Ellen Mosley-Thompson, who have published numerous influential papers in Science and Nature, showed us the center and explained their research to us, which involves many fields but especially ice core climatology. Since the 1970s, they have conducted research at the poles as well as on mountains near the equator (in Peru and Tibet), where they drill down and pull up the ice cores, then bring them down the mountain on yaks and trucks and eventually store them in a huge freezer, which you can see below. (Our brief tour of the freezer was the only time I wore my hat on this trip.) Drs. Thompson and Mosley-Thompson use the ice cores to infer details about the climate and history of a particular regionTEXTsort of like using tree rings. For example, from ice cores taken from Kilimanjaro, they found evidence of a 300-year drought 4000 years ago (evidenced by less snow and ice accumulation), which would have had a dramatic effect on societies at the time. With rapid climate change, unfortunately the glaciers are rapidly retreating, but a silver lining is that they’ve uncovered 5000 to 6000-year-old plants!

photo 7

Finally, I had looked forward to the discussions of the ongoing BICEP2 controversy, and I was not disappointed. Marc Kamionkowski (Johns Hopkins University) gave an excellent overview of the basics of cosmology, the expanding universe, cosmic microwave background radiation (CMB), which is sort of an “afterglow of the Big Bang.” Many collaborations using different telescopes (including researchers at UC San Diego) seek to detect CMB “B-mode” polarization of the CMB due to primordial gravitational waves, which would constitute evidence supporting the rapid “inflation” of the early universe and would be a momentous discovery! At the BICEP2’s press conference in March at Harvard and in the preprint, the scientists did say “if confirmed…”, but of course everyone was excited about the implications of the result. However, new measurements from the Planck collaboration (see below) suggest that the polarization might not be due to the CMB’s gravitational waves but to foreground emission from dust grains in our own galaxy, though their calculation of the dust contribution is highly uncertain.


A short discussion with Matthew Francis (freelance) and Betsy Mason (Wired) followed Kamionkowski’s talk, where they tackled questions that scientists and science communicators frequently face. Scientists want press attention and news outlets want headlines, so how should one describe and report caveats and uncertainties, especially when the implications (if confirmed) are so exciting? What is the best way to express skepticism of a particular aspect of a scientific result? And a question that I often ask: how can we communicate the messiness or “self-correcting” nature of science? In any case, we’ll all continue to follow the ongoing CMB debate in the scientific community and the media.

Now I’m looking forward to doing much more writing (and reading) and to participating in next year’s meeting!

Three Astrophysicists (including me) Meet with Congresswoman Davis

Last Tuesday, three weeks before the midterm election, three astrophysicists—graduate students and Ph.D. candidates Darcy Barron and Evan Grohs and I (a research scientist)—met with Representative Susan Davis (CA-53) and her staffer, Gavin Deeb. We had a twenty-minute meeting to talk about science in her district office in North Park, San Diego, which is on Adams Avenue and biking distance from my home. Darcy and I are her constituents, while Evan is a constituent of Rep. Scott Peters (CA-52), who is also a science advocate but is in a tight election race.

photo 1

I enjoyed participating in the Congressional Visit Day in Washington, DC, earlier this year (and Darcy had previously participated in the program too). In March, Josh Shiode (AAS Public Policy Fellow) and I had a short meeting with Rep. Davis and one of her DC staffers. This time in her San Diego district though, we had more time to chat. As before, she was very receptive to our message for federal investment in basic research, education and public outreach in the astronomical sciences and in science in general.

The current science budget situation and constraints from the ongoing “sequestration” leaves Congress and the Executive branch with little wiggle room, but we need to make the best of a bad situation. Otherwise, the US risks dropping behind Europe, Japan, and China in astrophysics research and in educating the next generation of scientists. Most federal funding for astronomy and astrophysics comes from the National Science Foundation (NSF), NASA, and the Department of Energy (DOE) Office of Science. Rather than improving and increasing these agencies’ constrained budgets, unfortunately Congress became mired in gridlock with little time before the election, and to avoid another government shutdown, Congress members had to vote on a “continuing resolution,” which basically keeps the budget on autopilot. Unless budget negotiations become an immediate priority after the election, it seems we’ll have to wait until FY 2016 to try to improve science budgets.

Rep. Davis stressed the importance of science communication, outreach, and improving diversity of the scientific workforce, and we were all in agreement about that. Communicating science to the public well helps to remind people how awesome science is and how important our investment in it is. And in our outreach efforts, the young and diverse students we reach and hope to inspire will be the people who advance science in the future. Rep. Davis was clearly interested in these issues and supportive of our and our colleagues’ work on them.

A couple months ago, Senator J. Rockefeller (D-WV), chair of the Committee on Commerce, Science, and Transportation, introduced the America COMPETES Reauthorization Act of 2014. According to the Association of American Universities, the bill calls for “robust but sustainable funding increases for the [NSF] and National Institute of Standards and Technology” (NIST) and it “recognizes the past success and continuing importance of the NSF’s merit review process.” It also supports each agency’s efforts to improve education of future science, technology, engineering, and math (STEM) professionals. But as Jeffrey Mervis of Science points out, support for COMPETES wasn’t sufficiently bipartisan and hasn’t been reauthorized.

On the other hand, perhaps there’s a better chance of Congress reauthorizing the Higher Education Act. The HEA is the major law that governs federal student aid, and it’s been reauthorized nine times since Pres. Johnson signed it into law in 1965. Considering that at least 70% of US university graduates are burdened with debt, this is clearly important. The HEA bill, introduced by Sen. Harkin (chair of the Health, Education, Labor and Pension Committee), would provide some relief for students by increasing state contributions to public universities (and thereby reduce tuition fees), supporting community colleges, and expanding programs that allow high school students to earn college credits. Disagreements between Democrats and Republicans remain on this bill, and we’ll have to wait and see in what form it will be passed.

We didn’t get into all these details, but I just wanted to give you some context. We also briefly discussed the need for graduate education reform and for preparing graduate students for the difficult job markets they face. These issues aren’t addressed in the HEA, though that bill would benefit some grad students who would have decreased loan burdens.

In any case, we’ve got to continue our work and our scientific advocacy, and after the November election, we hope that Rep. Davis, Rep. Peters (or DeMaio), and other Congressional lawmakers can get back together and negotiate a better budget for basic research, education, and public outreach in the physical and social sciences.

One Man’s Perspective on Diversity and Inequality in Science

[This blog was originally posted at the Women in Astronomy blog. Thanks to Jessica Kirkpatrick for editing assistance.]

It’s obvious, but one thing I’ve noticed over my career so far is that many departments, institutions, conferences, organizations, committees, high-profile publications, big research grants, etc., both nationally and internationally, and especially leadership positions, are filled with straight, white, men. There are notable and impressive exceptions, but the trend is clear. The distributions of people in the scientific workforce clearly don’t reflect their distribution in the overall population. For example, according to the AAS’s Committee on the Status of Women in Astronomy, nearly half of undergraduate students who obtain bachelors of science degrees are women, but only a third of astronomy graduate students and 30% of Ph.D. recipients are. Women compose 25-30% of postdocs and lower-level faculty, and this drops by half (to 15%) of tenured faculty. This is not explained by historical differences in gender: if women were promoted and retained at rates comparable to men, then the fractions advancing to higher career stages should be equal. The demographics in terms of race aren’t good either: according to the American Institute of Physics, African Americans and Hispanics combined account for only 5% of physics faculty.

Of course, this isn’t news to readers of this blog. And the disturbing lack of diversity doesn’t just affect us in astronomy and astrophysics or even just in the physical sciences. For example, as you’ve probably seen, the lack of diversity in Silicon Valley has deservedly been in the news lately. Tech companies like Google, Yahoo, Facebook, LinkedIn, and Twitter have all been criticized for being dominated by white men (and recently, also Asian men). We definitely need to work more at improving diversity in all STEM fields.

I’m a half-white half-Iranian man in astronomy and astrophysics. Everything is competitive these days, but in my opinion, if I’m applying for a job or a grant, for example, and if a black or Latino person or a woman with the same experience and qualifications as me has also applied, I think she should probably get it. While the grant and job markets in STEM fields are very competitive, I think we should look at the big picture, in which we need to strive for more equality in our universities and institutions. It’s also important to keep in mind that both men and women leave academia (though at different rates) and find important and fulfilling careers elsewhere.

I’d also like to point out that, in Iran, STEM fields are not seen as “male” subjects as much as they are in the US and they therefore have almost gender parity in these fields. For example, 70% of Iranian science and engineering students are women and when I last visited Tehran, I met many brilliant female Iranian physics students who could speak about science in both Farsi and English. And in recent news, Maryam Mirzakhani recently became the first woman to win the Fields Medal mathematics prize, and Azeen Ghorayshi won the Clark/Payne award for science journalists. (She recently wrote a story for Newsweek about smog in Tehran, which keeps getting worse.)

In any case, we all benefit—and science benefits—when we have a diverse community. A more diverse workforce, including among leadership positions, helps to produce new ideas and perspectives and to guard against bias. In business, when there is more diversity, everyone profits.

Speaking of bias, how can we deal with “unconscious bias”? In practice, two applicants are never identical, so we have to assess people on a case-by-case basis. It turns out that both men and women surprisingly have similar biases against women in STEM fields, though the biases can be reduced when people are more aware of them and when diverse committees make decisions about hiring and leadership decisions. In addition, diversity and racial equity should be considered at both the initial and shortlist stages of admissions and hiring, and the academic tenure system should be more flexible.

On the issue of leadership and mentorship, I’ve had female and male bosses and mentors and I’ve advised male and female students; in addition, I’ve worked with and for people from many different countries and backgrounds. As far as I can tell, it hasn’t made a difference for me, and I’ve seen a greater variety of management and collaboration styles among men and women than differences between them (though I’ve seen more overconfident men than women). Unfortunately though, some people still do view female leaders differently and hold them to different standards than male leaders.

So what can we do? Many people who are trying to improve this situation are doing excellent work on public outreach and educational programs especially with the purpose of reaching and encouraging girls, minorities, and underprivileged youth. It’s not hard to find such programs everywhere: for example, I recently participated in the Adler Planetarium Astrojournalists program and in a physics outreach program at UCSD with Intertribal Youth organized by Adam Burgasser. The success of such programs, as well as the growing numbers of role models, help to gradually changing cultural stereotypes and reducing biases. For those of us at research institutions, work on outreach and communication should be valued as much as research achievements when hiring and tenure decisions are made. One way to do this would be to explicitly state this in job advertisements and to hiring and tenure committees at our own institutions. It would require more work from such committees, but that’s a small price to pay.

The literature on “confidence gap” issues has been growing rapidly, encouraging women to “lean in” and be more confident and self-assured at work. This is important, but it amounts to encouraging women to behave more like men. We can’t neglect persistent structural and institutionalized barriers and we can’t forget that gender and race inequality is everywhere or that men and white people are benefit from their privileges. (And although class is a separate issue, it’s worth pointing out that there is also a lack of class diversity within higher education and graduate programs. According to the US Census Bureau, the income discrepancy between the working class and the professional class with higher academic degrees is growing, so this problem is getting worse.)

Increasing gender and race diversity is an important goal and an ongoing struggle, but it’s also a means to an end. I believe that we need to set our sights higher than merely having a few more white women and people of color among faculty members. We need *paid* maternity leave, universal or child-care options, better dual-career policies, and paternity leave should be expected. We shouldn’t praise men who continue working full-time rather than spending time with their new children, and we still have a long way to go until men share housework equally with women. Work-life balance issues affect everyone, including single people and those without children. We can advocate for new policies at our own institutions, and city- or state-wide or even national policies would make a huge difference. As a potential step in that direction, even Congress is aware of these issues: in the America COMPETES Act (which funds STEM R&D, education, and innovation) reauthorization it’s now directing the Office of Science & Technology Policy (OSTP) develop and implement guidelines for policies that encourage work-life balance, workplace flexibility, and family-responsiveness. In any case, we’re gradually making progress, but much more work remains to be done.

Rise of the Giant Telescopes

The biggest telescope ever constructed, the Thirty Meter Telescope (TMT), officially broke ground on Mauna Kea in Hawai’i on Tuesday. Building on technology used for the Keck telescopes, the TMT’s primary mirror will be segmented combining 492 hexagonal reflectors that will be honeycombed together, and it will have an effective diameter of 30 meters, as you’ve probably guessed. (Astrophysicists come up with very descriptive names for their telescopes and simulations.) 30 meters is really really big—about a third the length of an American football field and nearly the size of a baseball diamond’s infield. When it’s built it will look something like this:


(If you’re interested, here’s a shameless plug: we discussed the TMT’s groundbreaking on the Weekly Space Hangout with Universe Today yesterday, and you can see the video on YouTube.)

The groundbreaking and blessing ceremony, which included George Takei hosting a live webcast, didn’t go quite as planned. It was disrupted by a peaceful protest of several dozen people who oppose the telescope’s construction. The protesters chanted and debated with attendees and held signs with “Aloha ‘Aina” (which means ‘love of the land’) and using TMT to spell out “Too Many Telescopes.” There has been a history of tension over what native Hawaiians say is sacred ground in need of protection and is also one of the best places on Earth to place telescopes. This is a longstanding issue, and the tension between them back in 2001 was reported in this LA Times article. According to Garth Illingworth, co-chair of the Science Advisory Committee, “It was an uncomfortable situation for those directly involved, but the way in which the interactions with the protesters was handled, with considerable effort to show respect and to deal with the situation with dignity, reflected credit on all concerned.” In any case, construction will continue as planned.


The TMT’s science case includes observing distant galaxies and the large-scale structure of the early universe, and will enable new research on supermassive black holes, and star and planet formation. The TMT is led by researchers at Caltech and University of California (where I work), and Canada, Japan, China, India. Its optical to near-infrared images will be deeper and sharper than anything else available, with spatial resolution twelve times that of the Hubble Space Telescope and eight times the light-gathering area of any other optical telescope. If it’s completed on schedule, it will have “first light” in 2022 and could be the first of the next generation of huge ground-based telescopes. The others are the European Extremely Large Telescope (E-ELT, led by the European Southern Observatory) and the Giant Magellan Telescope (GMT, led by the Carnegie Observatories and other institutions), which will be located in northern Chile.

Every ten years, astronomers and astrophysicists prioritize small-, medium-, and large-scale ground-based and space-based missions, with the aim of advising the federal government’s investment, such as funding through the National Science Foundation (NSF) and NASA. The most recent decadal survey, conducted by the National Academy of Sciences is available online (“New Worlds, New Horizons in Astronomy and Astrophysics“). For the large-scale ground-based telescopes, the NSF will be providing funding for the Large Synoptic Survey Telescope (which I’ve written about here before) and the TMT. There had been debates about funding either the TMT or the GMT, but not both, though a couple years ago GMT scientists opted out of federal funding (see this Science article). NASA is focusing on space-based missions such as the upcoming James Webb Space Telescope (JWST) and Wide-Field InfraRed Survey Telescope (WFIRST), which will be launched later this decade.

Is “Data-driven Science” an Oxymoron?

In recent years, we’ve been repeatedly told that we’re living and working in an era of Big Data (and Big Science). We’ve heard how Nate Silver and others are revolutionizing how we analyze and interpret data. In many areas of science and in many aspects of life, for that matter, we’re obtaining collections of datasets so large and complex that it becomes necessary to change our traditional analysis methods. Since the volume, velocity, and variety of data are rapidly increasing, it is increasingly important to develop and apply appropriate techniques and statistical tools.

However, is it true that Big Data changes everything? Much can be gained from proper data analysis and from “data-driven science.” For example, the popular story about Billy Beane and Moneyball shows how Big Data and statistics transformed how baseball teams are assessed. But I’d like to point out some misconceptions and dangers of the concept of data-driven science.

Governments, corporations, and employers are already collecting (too?) much of our precious, precious data and expending massive effort to study it. We might worry about this because of concerns of privacy, but we should also worry about what might happen to analyses that are excessively focused on the data. There are questions that we should be asking more often: Who’s collecting the data? Which data and why? Which analysis tools and why? What are their assumptions and priors? My main point will be that the results from computer codes churning through massive datasets are not objective or impartial, and the data don’t inevitably drive us to a particular conclusion. This is why the concept of “data-driven” anything is misleading.

Let’s take a look at a few examples of data-driven analysis that have been in the news lately…

Nate Silver and FiveThirtyEight

Many media organizations are about something, and they use a variety of methods to study it. In a sense, FiveThirtyEight isn’t really about something. (If I wanted to read about nothing, I’d check out ClickHole and be more entertained.) Instead, FiveThirtyEight is about their method, which they call “data journalism” and by which they mean “statistical analysis, but also data visualization, computer programming and data-literate reporting.”

I’m exaggerating though. They cover broad topics related to politics, economics, science, life, and sports. They’ve had considerable success making probabilistic predictions about baseball, March Madness, and World Cup teams and in packaging statistics in a slick and easy-to-understand way. They also successfully predicted the 2012 US elections on a state-by-state basis, though they stuck to the usual script of treating it as a horse race: one team against another. Their statistical methods are sometimes “black boxes”, but if you look, they’ll often provide additional information about them. Their statistics are usually sound, but maybe they should be more forthcoming about the assumptions and uncertainties involved.

Their “life” section basically allows them to cover whatever they think is the popular meme of the day, which in my opinion isn’t what a non-tabloid media organization should be focused on doing. This section includes their “burrito competition,” which could be a fun idea but their bracket apparently neglected sparsely-populated states like New Mexico and Arizona, where the burrito historically originated.

The “economics” section has faced substantial criticism. For example, Ben Casselman’s article, “Typical minimum-wage earners aren’t poor, but they’re not quite middle class,” was criticized in Al-Jazeera America for being based on a single set of data plotting minimum-wage workers by household income. He doesn’t consider the controversial issue of how to measure poverty or the decrease in the real value of the minimum wage, and he ends up undermining the case for raising the minimum wage. Another article about corporate cash hoarding was criticized by Paul Krugman and others for jumping to conclusions based on revised data. As Malcolm Harris (an editor at The New Inquiry) writes, “Data extrapolation is a very impressive trick when performed with skill and grace…but it doesn’t come equipped with the humility we should demand from our writers.”

Their “science” section leaves a lot to be desired. For example, they have a piece assessing health news reports in which the author (Jeff Leek) uses Bayesian priors based on an “initial gut feeling” before assigning numbers to a checklist. As pointed out in this Columbia Journalism Review article, “plenty of people have already produced such checklists—only more thoughtfully and with greater detail…Not to mention that interpreting the value of an individual scientific study is difficult—a subject worthy of much more description and analysis than FiveThirtyEight provides.” And then there was the brouhaha about Roger Pielke, whose writings about the effects of climate change I criticized before, and who’s now left the organization.

Maybe Nate Silver should leave these topics to the experts and stick to covering sports? He does that really well.

Thomas Piketty on Inequality

Let’s briefly consider two more examples. You’ve probably heard about the popular and best-selling analysis of data-driven economics in Thomas Piketty’s magnum opus, Capital in the Twenty-first Century. It’s a long but well-written book in which Piketty makes convincing arguments about how income and wealth inequality are worsening in the United States, France, and other developed countries. (See these reviews in the NY Review of Books and Slate.) It’s influential because of its excellent and systematic use of statistics and data analysis, because of the neglect of wealth inequality by other mainstream economists, and of course because of the economic recession and the dominance of the top 1 percent.

Piketty has been criticized by conservatives, and he has successfully responded to these critics. His proposal for a progressive tax on wealth has also been criticized by some. Perhaps the book’s popularity and the clearly widespread and underestimated economic inequality will result in more discussion and consideration of this and other proposals.

I want to make a different point though. As impressive as Piketty’s book is, we should be careful about how we interpret it and his ideas for reducing inequality. For example, as argued by Russell Jacoby, unlike Marx in Das Kapital, Piketty takes the current system of capitalism for granted. Equality “as an idea and demand also contains an element of resignation; it accepts society, but wants to balance out the goods or privileges…Equalizing pollution pollutes equally, but does not end pollution.” While Piketty’s ideas for reducing economic extremes could be very helpful, they don’t “address a redundant labor force, alienating work, or a society driven by money and profit.” You may or may not agree with Piketty’s starting point—and you do have to start somewhere—but it’s important to keep it in mind when interpreting the results.

As before, just because something is “data-driven” doesn’t mean that the data, analysis, or conclusions can’t be questioned. We always need to be grounded in data, but we need to be careful about how we interpret analyses of them.

HealthMap on Ebola

Harvard’s HealthMap gained attention for using algorithms to detect the beginning of the Ebola outbreak in Africa before the World Health Organization did. Is that a big success for “big data”? Not so, according to Foreign Policy. “It’s an inspirational story that is a common refrain in the ‘big data’ world—sophisticated computer algorithms sift through millions of data points and divine hidden patterns indicating a previously unrecognized outbreak that was then used to alert unsuspecting health authorities and government officials…The problem is that this story isn’t quite true.” By the time HealthMap monitored its very first report, the Guinean government had actually already announced the outbreak and notified the WHO. Part of the problem is that it was published in French, while most monitoring systems today emphasize English-language material.

This seems to be another case of people jumping to conclusions to fit a popular narrative.

What does all this mean for Science?

Are “big data” and “data-driven” science more than just buzzwords? Maybe. But as these examples show, we have to be careful when utilizing them and interpreting their results. When some people conduct various kinds of statistical analyses and data mining, they act as if the data speak for themselves. So their conclusions must be indisputable! But the data never speak for themselves. We scientists and analysts are not simply going around performing induction, collecting every relevant datum around us, and cranking the data through machines.

Every analysis has some assumptions. We all make assumptions about which data to collect, which way to analyze them, which models to use, how to reduce our biases, and how to assess our uncertainties. All machine learning methods, including “unsupervised” learning (in which one tries to find hidden patterns in data), require assumptions. The data definitely do not “drive” one to a particular conclusion. When we interpret someone’s analysis, we may or may not agree with their assumptions, but we should know what they are. And any analyst who does not clearly disclose their assumptions and uncertainties is doing everyone a disservice. Scientists are human and make mistakes, but these are obvious mistakes to avoid. Although objective data-driven science might not be possible, as long as we’re clear about how we choose our data and models and how we analyze them, then it’s still possible to make progress and reach a consensus on some issues and ask new questions on others.