This is the first of two articles whose genesis was the nexus of hurricanes and data visualisation. The second article, Part II – Map Reading, has now been published.
Introduction
This first article is not a critique of Thomas Pynchon‘s celebrated work, instead it refers to a grave malady that can afflict otherwise health data visualisations; the use and abuse of rainbow colours. This is an area that some data visualisation professionals can get somewhat hot under the collar about; there is even a Twitter hashtag devoted to opposing this colour choice, #endtherainbow.
The [mal-] practice has come under additional scrutiny in recent weeks due to the major meteorological events causing so much damage and even loss of life in the Caribbean and southern US; hurricanes Harvey and Irma. Of course the most salient point about these two megastorms is their destructive capability. However the observations that data visualisers make about how information about hurricanes is conveyed do carry some weight in two areas; how the public perceives these phenomena and how they perceive scientific findings in general [1]. The issues at stake are ones of both clarity and inclusiveness. Some of these people felt that salt was rubbed in the wound when the US National Weather Service, avid users of rainbows [2], had to add another colour to their normal palette for Harvey:
In 2015, five scientists collectively wrote a letter to Nature entitled “Scrap rainbow colour scales” [3]. In this they state:
It is time to clamp down on the use of misleading rainbow colour scales that are increasingly pervading the literature and the media. Accurate graphics are key to clear communication of scientific results to other researchers and the public — an issue that is becoming ever more important.
At this point I have to admit to using rainbow colour schemes myself professionally and personally [4]; it is often the path of least resistance. I do however think that the #endtherainbow advocates have a point, one that I will try to illustrate below.
Many Marvellous Maps
Let’s start by introducing the idyllic coastal county of Thomasshire, a map of which appears below:
Of course this is a cartoon map, it might be more typical to start with an actual map from Google Maps or some other provider [5], but this doesn’t matter to the argument we will construct here. Let’s suppose that – rather than anything as potentially catastrophic as a hurricane – the challenge is simply to record the rainfall due to a nasty storm that passed through this shire [6]. Based on readings from various weather stations (augmented perhaps by information drawn from radar), rainfall data would be captured and used to build up a rain contour map, much like the elevation contour maps that many people will recall from Geography lessons at school [7].
If we were to adopt a rainbow colour scheme, then such a map might look something like the one shown below:
Here all areas coloured purple will have received between 0 and 10 cm of rain, blue between 10 and 20 cm of rain and so on.
At this point I apologise to any readers who suffer from migraine. An obvious drawback of this approach is how garish it is. Also the solid colours block out details of the underlying map. Well something can be done about both of these issues by making the contour colours transparent. This both tones them down and allows map details to remain at least semi-visible. This gets us a new map:
Here we get into the core of the argument about the suitability of a rainbow palette. Again quoting from the Nature letter:
[…] spectral-type colour palettes can introduce false perceptual thresholds in the data (or hide genuine ones); they may also mask fine detail in the data. These palettes have no unique perceptual ordering, so they can de-emphasize data extremes by placing the most prominent colour near the middle of the scale.
[…]
Journals should not tolerate poor visual communication, particularly because better alternatives to rainbow scales are readily available (see NASA Earth Observatory).
In our map, what we are looking to do is to show increasing severity of the deluge as we pass from purple (indigo / violet) up to red. But the ROYGBIV [8] colours of the spectrum are ill-suited to this. Our eyes react differently to different colours and will not immediately infer the gradient in rainfall that the image is aiming to convey. The NASA article the authors cite above uses a picture to paint a thousand words:
Compared to a monochromatic or grayscale palette the rainbow palette tends to accentuate contrast in the bright cyan and yellow regions, but blends together through a wide range of greens. Sourced from NASA
Another salient point is that a relatively high proportion of people suffer from one or other of the various forms of colour blindness [9]. Even the most tastefully pastel rainbow chart will disadvantage such people seeking to derive meaning from it.
Getting Over the Rainbow
So what could be another approach? Well one idea is to show gradients of whatever the diagram is tracking using gradients of colour; this is the essence of the NASA recommendation. I have attempted to do just this in the next map.
I chose a bluey-green tone both as it was to hand in the Visio palette I was using and also to avoid confusion with the blue sea (more on this later). Rather than different colours, the idea is to map intensity of rainfall to intensity of colour. This should address both colour-blindness issues and the problems mentioned above with discriminating between ROYGBIV colours. I hope that readers will agree that it is easier to grasp what is happening at a glance when looking at this chart than in the ones that preceded it.
However, from a design point of view, there is still one issue here; the sea. There are too many bluey colours here for my taste, so let’s remove the sea colouration to get:
Some purists might suggest also turning the land white (or maybe a shade of grey), others would mention that the grid-lines add little value (especially as they are not numbered). Both would probably have a point, however I think that use can also push minimalism too far. I am pretty happy that our final map delivers the information it is intended to convey much more accurately and more immediately than any of its predecessors.
Comparing the first two rainbow maps to this last one, it is perhaps easy to see why so many people engaged in the design of data visualisations want to see an end to ROYGBIV palettes. In the saying, there is a pot of gold at the end of the rainbow, but of course this can never be reached. I strongly suspect that, despite the efforts of the #endtherainbow crowd, an end to the usage of this particular palette will be equally out of reach. However I hope that this article is something that readers will bear in mind when next deciding on how best to colour their business graph, diagram or data visualisation. I am certainly going to try to modify my approach as well.
The story of hurricanes and data visualisation will continue in Part II – Map Reading.
Notes
[1]
For some more thoughts on the public perception of science, see Toast.
[2]
I guess it’s appropriate from at least one point of view.
Ed Hawkins – National Centre for Atmospheric Science, University of Reading, UK (@ed_hawkins)
Doug McNeall – Met Office Hadley Centre, Exeter, UK (@dougmcneall)
Jonny Williams – University of Bristol, UK (LinkedIn page)
David B. Stephenson – University of Exeter, UK (Academic page)
David Carlson – World Meteorological Organization, Geneva, Switzerland (retired June 2017).
[4]
I did also go through a brief monochromatic phase, but it didn’t last long.
[5]
I guess it might take some time to find Thomasshire on Google Maps.
[6]
Based on the data I am graphing here, it was a very nasty storm indeed! In this article, I am not looking for realism, just to make some points about the design of diagrams.
Whereas contours on a physical geography map (see above) link areas with the same elevation above sea level, rainfall contour lines would link areas with the same precipitation.
[8]
Red, Orange, Yellow, Green, Blue, Indigo, Violet.
[9]
Red–green color blindness, the most common sort, affects 80 in 1,000 of males and 4 in 1,000 of females of Northern European descent.
This article is about the wisdom of the crowd [1], or more particularly its all too frequent foolishness. I am going to draw on a paper recently published in Nature by a cross-disciplinary team from the Massachusetts Institute of Technology and Princeton University. The authors are Dražen Prelec, H. Sebastian Seung and John McCoy. The paper’s title is A solution to the single-question crowd wisdom problem[2]. Rather than reinvent the wheel, here is a section from the abstract (with my emphasis):
Once considered provocative, the notion that the wisdom of the crowd is superior to any individual has become itself a piece of crowd wisdom, leading to speculation that online voting may soon put credentialed experts out of business. Recent applications include political and economic forecasting, evaluating nuclear safety, public policy, the quality of chemical probes, and possible responses to a restless volcano. Algorithms for extracting wisdom from the crowd are typically based on a democratic voting procedure. […] However, democratic methods have serious limitations. They are biased for shallow, lowest common denominator information, at the expense of novel or specialized knowledge that is not widely shared.
The Problems
The authors describe some compelling examples of where a crowd-based approach ignores the aforementioned specialised knowledge. I’ll cover a couple of these in a second, but let me first add my own.
Suppose we ask 1,000 people to come up with an estimate of the mass of a proton. One of these people happens to have won the Nobel Prize for Physics the previous year. Is the average of the estimates provided by the 1,000 people likely to be more accurate, or is the estimate of the one particularly qualified person going to be superior? There is an obvious answer to this question [3].
Lest it be thought that the above flaw in the wisdom of the crowd is confined to populations including a Nobel Laureate, I’ll reproduce a much more quotidian example from the Nature paper [4].
[..] imagine that you have no knowledge of US geography and are confronted with questions such as: Philadelphia is the capital of Pennsylvania, yes or no? And, Columbia is the capital of South Carolina, yes or no? You pose them to many people, hoping that majority opinion will be correct. [in an actual exercise the team carried out] this works for the Columbia question, but most people endorse the incorrect answer (yes) for the Philadelphia question. Most respondents may only recall that Philadelphia is a large, historically significant city in Pennsylvania, and conclude that it is the capital. The minority who vote no probably possess an additional piece of evidence, that the capital is Harrisburg. A large panel will surely include such individuals. The failure of majority opinion cannot be blamed on an uninformed panel or flawed reasoning, but represents a defect in the voting method itself.
I’m both a good and bad example here. I know the capital of Pennsylvania is Harrisburg because I have specialist knowledge [5]. However my acquaintance with South Carolina is close to zero. I’d therefore get the first question right and have a 50 / 50 chance on the second (all other things being equal of course). My assumption is that Columbia is, in general, much more well-known than Harrisburg for some reason.
The authors go on to cover the technique that is often used to try to address this type of problem in surveys. Respondents are also asked how confident they are about their answer. Thus a tentative “yes” carries less weight than a definitive “yes”. However, as the authors point out, such an approach only works if correct responses are strongly correlated with respondent confidence. As is all too evident from real life, people are often both wrong and very confident about their opinion [6]. The authors extended their Philadelphia / Columbia study to apply confidence weightings, but with no discernible improvement.
A Surprisingly Popular Solution
As well as identifying the problem, the authors suggest a solution and later go on to demonstrate its efficacy. Again quoting from the paper’s abstract:
Here we propose the following alternative to a democratic vote: select the answer that is more popular than people predict. We show that this principle yields the best answer under reasonable assumptions about voter behaviour, while the standard ‘most popular’ or ‘most confident’ principles fail under exactly those same assumptions.
Let’s use the examples of capitals of states again here (as the authors do in the paper). As well as asking respondents, “Philadelphia is the capital of Pennsylvania, yes or no?” you also ask them “What percentage of people in this survey will answer ‘yes’ to this question?” The key is then to compare the actual survey answers with the predicted survey answers.
As shown in the above exhibit, in the authors’ study, when people were asked whether or not Columbia is the capital of South Carolina, those who replied “yes” felt that the majority of respondents would agree with them. Those who replied “no” symmetrically felt that the majority of people would also reply “no”. So no surprises there. Both groups felt that the crowd would agree with their response.
However, in the case of whether or not Philadelphia is the capital of Pennsylvania there is a difference. While those who replied “yes” also felt that the majority of people would agree with them, amongst those who replied “no”, there was a belief that the majority of people surveyed would reply “yes”. This is a surprise. People who make the correct response to this question feel that the wisdom of the crowd will be incorrect.
In the Columbia example, what people predict will be the percentage of people replying “yes” tracks with the actual response rate. In the Philadelphia example, what people predict will be the percentage of people replying “yes” is significantly less than the actual proportion of people making this response [7]. Thus a response of “no” to “Philadelphia is the capital of Pennsylvania, yes or no?” is surprisingly popular. The methodology that the authors advocate would then lead to the surprisingly popular answer (i.e. “no”) actually being correct; as indeed it is. Because there is no surprisingly popular answer in the Columbia example, then the result of a democratic vote stands; which is again correct.
To reiterate: a surprisingly popular response will overturn the democratic verdict, if there is no surprisingly popular response, the democratic verdict is unmodified.
As well as confirming the superiority of the surprisingly popular approach (as opposed to either weighted or non-weighted democratic votes) with questions about state capitals, the authors went on to apply their new technique in a range of other areas [8].
Study 1 used 50 US state capitals questions, repeating the format [described above] with different populations [9].
Study 2 employed 80 general knowledge questions.
Study 3 asked professional dermatologists to diagnose 80 skin lesion images as benign or malignant.
Study 4 presented 90 20th century artworks [see the images above] to laypeople and art professionals, and asked them to predict the correct market price category.
Taking all responses across the four studies into account [10], the central findings were as follows [11]:
We first test pairwise accuracies of four algorithms: majority vote, surprisingly popular (SP), confidence-weighted vote, and max. confidence, which selects the answer endorsed with highest average confidence.
Across all items, the SP algorithm reduced errors by 21.3% relative to simple majority vote (P < 0.0005 by two-sided matched-pair sign test).
Across the items on which confidence was measured, the reduction was:
35.8% relative to majority vote (P < 0.001),
24.2% relative to confidence-weighted vote (P = 0.0107) and
22.2% relative to max. confidence (P < 0.13).
The authors go on to further kick the tyres [12] on these results [13] without drawing any conclusions that deviate considerably from the ones they first present and which are reproduced above. The surprising finding is that the surprisingly popular algorithm significantly out-performs the algorithms normally used in wisdom of the crowd polling. This is a major result, in theory at least.
Some Thoughts
At the end of the abstract, the authors state that:
Like traditional voting, [the surprisingly popular algorithm] accepts unique problems, such as panel decisions about scientific or artistic merit, and legal or historical disputes. The potential application domain is thus broader than that covered by machine learning […].
Given the – justified – attention that has been given to machine learning in recent years, this is a particularly interesting claim. More broadly, SP seems to bring much needed nuance to the wisdom of the crowd. It recognises that the crowd may often be right, but also allows better informed minorities to override the crowd opinion in specific cases. It does this robustly in all of the studies that the authors conducted. It will be extremely interesting to see this novel algorithm deployed in anger, i.e. in a non-theoretical environment. If its undoubted promise is borne out – and the evidence to date suggests that it will be – then statisticians will have a new and powerful tool in their arsenal and a range of predictive activities will be improved.
The scope of applicability of the SP technique is as wide as that of any wisdom of the crowd approach and, to repeat the comments made by the authors in their abstract, has recently included:
[…] political and economic forecasting, evaluating nuclear safety, public policy, the quality of chemical probes, and possible responses to a restless volcano
If the author’s initial findings are repeated in “live” situations, then the refinement to the purely democratic approach that SP brings should elevate an already useful approach to being an indispensable one in many areas.
I will let the authors have a penultimate word [14]:
Although democratic methods of opinion aggregation have been influential and productive, they have underestimated collective intelligence in one respect. People are not limited to stating their actual beliefs; they can also reason about beliefs that would arise under hypothetical scenarios. Such knowledge can be exploited to recover truth even when traditional voting methods fail. If respondents have enough evidence to establish the correct answer, then the surprisingly popular principle will yield that answer; more generally, it will produce the best answer in light of available evidence. These claims are theoretical and do not guarantee success in practice, as actual respondents will fall short of ideal. However, it would be hard to trust a method [such as majority vote or confidence-weighted vote] if it fails with ideal respondents on simple problems like [the Philadelphia one]. To our knowledge, the method proposed here is the only one that passes this test.
The ultimate thought I will present in this article is an entirely speculative one. The authors posit that their method could be applied to “potentially controversial topics, such as political and environmental forecasts”, while cautioning that manipulation should be guarded against. Their suggestion leads me wonder what impact on the results of opinion polls a suitably formed surprisingly popular questionnaire would have had in the run up to both the recent UK European Union Referendum and the plebiscite for the US Presidency. Of course it is now impossible to tell, but maybe some polling organisations will begin to incorporate this new approach going forward. It can hardly make things worse.
Notes
[1]
According to Wikipedia, the phenomenon that:
A large group’s aggregated answers to questions involving quantity estimation, general world knowledge, and spatial reasoning has generally been found to be as good as, and often better than, the answer given by any of the individuals within the group.
The authors of the Nature paper question whether this is true in all circumstances.
[2]
Prelec, D., Seung, H.S., McCoy, J., (2017). A solution to the single-question crowd wisdom problem. Nature 541, 532–535.
You can view a full version of this paper care of Springer Nature SharedIt at the following link. ShareIt is Springer’s content sharing initiative.
Direct access to the article on Nature’s site (here) requires a subscription to the journal.
[3]
This example is perhaps an interesting rejoinder to the increasing lack of faith in experts in the general population, something I covered in Toast.
Of course the answer is approximately: 1.6726219 × 10-27 kg.
[4]
I have lightly edited this section but abjured the regular bracketed ellipses (more than one […] as opposed to conic sections as I note elsewhere). This is both for reasons of readability and also as I have not yet got to some points that the authors were making in this section. The original text is a click away.
[5]
My wife is from this state.
[6]
Indeed it sometimes seems that the more wrong the opinion, the more certain that people believe it to be right.
Here the reader is free to insert whatever political example fits best with their worldview.
[7]
Because many people replying “no” felt that a majority would disagree with them.
[8]
Again I have lightly edited this text.
[9]
To provide a bit of detail, here the team created a questionnaire with 50 separate questions sets of the type:
{Most populous city in a state} is the capital of {state}: yes or no?
How confident are you in your answer (50- 100%)?
What percentage of people surveyed will respond “yes” to this question? (1 – 100%)
This was completed by 83 people split between groups of undergraduate and graduate students at both MIT and Princeton. Again see the paper for further details.
[10]
And eliding some nuances such as some responses being binary (yes/no) and others a range (e.g. the dermatologists were asked to rate the chance of malignancy on a six point scale from “absolutely uncertain to absolutely certain”). Also respondents were asked to provide their confidence in some studies and not others.
[11]
Once more with some light editing.
[12]
This is a technical term employed in scientific circles an I apologise if my use of jargon confuses some readers.
[13]
Again please see the actual paper for details.
[14]
Modified very slightly by my last piece of editing.
This blog touches on a wide range of topics, including social media, cultural transformation, general technology and – last but not least – sporting analogies. However, its primary focus has always been on data and information-centric matters in a business context. Having said this, all but the more cursory of readers will have noted the prevalence of pieces with a Mathematical or Scientific bent. To some extent this is a simple reflection of the author’s interests and experience, but a stronger motivation is often to apply learnings from different fields to the business data arena. This article is probably more scientific in subject matter than most, but I will also look to highlight some points pertinent to commerce towards the end.
Introduction
The topic I want to turn my attention to in this article is public trust in science. This is a subject that has consumed many column inches in recent years. One particular area of focus has been climate science, which, for fairly obvious political reasons, has come in for even more attention than other scientific disciplines of late. It would be distracting to get into the arguments about climate change and humanity’s role in it here [1] and in a sense this is just the latest in a long line of controversies that have somehow become attached to science. An obvious second example here is the misinformation circling around both the efficacy and side effects of vaccinations [2]. In both of these cases, it seems that at least a sizeable minority of people are willing to query well-supported scientific findings. In some ways, this is perhaps linked to the general mistrust of “experts” and “elites” [3] that was explicitly to the fore in the UK’s European Union Referendum debate [4].
“People in this country have had enough of experts”
– Michael Gove [5], at this point UK Justice Secretary and one of the main proponents of the Leave campaign, speaking on Sky News, June 2016.
Mr Gove was talking about economists who held a different point of view to his own. However, his statement has wider resonance and cannot be simply dismissed as the misleading sound-bite of an experienced politician seeking to press his own case. It does indeed appear that in many places around the world experts are trusted much less than they used to be and that includes scientists.
“Many political upheavals of recent years, such as the rise of populist parties in Europe, Donald Trump’s nomination for the American presidency and Britain’s vote to leave the EU, have been attributed to a revolt against existing elites.”
A Brief [6] History of the Public Perception of Science
Note: This section is focussed on historical developments in the public’s trust in science. If the reader would like to skip on to more toast-centric content, then please click here.
Answering questions about the erosion of trust in politicians and the media is beyond the scope of this humble blog. Wondering what has happened to trust in science is firmly in its crosshairs. One part of the answer is that – for some time – scientists were held in too much esteem and the pendulum was inevitably going to swing back the other way. For a while the pace of scientific progress and the miracles of technology which this unleashed placed science on a pedestal from which there was only one direction of travel. During this period in which science was – in general – uncritically held in great regard, the messy reality of actual science was never really highlighted. The very phrase “scientific facts” is actually something of an oxymoron. What we have is instead scientific theories. Useful theories are consistent with existing observations and predict new phenomena. However – as I explained in Patterns patterns everywhere – a theory is only as good as the latest set of evidence and some cherished scientific theories have been shown to be inaccurate; either in general, or in some specific circumstances [7]. However saying “we have a good model that helps us explain many aspects of a phenomenon and predict more, but it doesn’t cover everything and there are some uncertainties” is a little more of a mouthful than “we have discovered that…”.
There have been some obvious landmarks along the way to science’s current predicament. The unprecedented destruction unleashed by the team working on the Manhattan Project at first made the scientists involved appear God-like. It also seemed to suggest that the path to Great Power status was through growing or acquiring the best Physicists. However, as the prolonged misery caused in Japan by the twin nuclear strikes became more apparent and as the Cold War led to generations living under the threat of mutually assured destruction, the standing attached by the general public to Physicists began to wane; the God-like mantle began to slip. While much of our modern world and its technology was created off the back of now fairly old theories like Quantum Chromodynamics and – most famously – Special and General Relativity, the actual science involved became less and less accessible to the man or woman in the street. For all the (entirely justified) furore about the detection of the Higgs Boson, few people would be able to explain much about what it is and how it fits into the Standard Model of particle physics.
In the area of medicine and pharmacology, the Thalidomide tragedy, where a drug prescribed to help pregnant women suffering from morning sickness instead led to terrible birth defects in more than 10,000 babies, may have led to more stringent clinical trials, but also punctured the air of certainty that had surrounded the development of the latest miracle drug. While medical science and related disciplines have vastly improved the health of much of the globe, the glacial progress in areas such as oncology has served as a reminder of the fallibility of some scientific endeavours. In a small way, the technical achievements of that apogee of engineering, NASA, were undermined by loss of crafts and astronauts. Most notably the Challenger and Columbia fatalities served to further remove the glossy veneer that science had acquired in the 1940s to 1960s.
Lest it be thought at this point that I am decrying science, or even being anti-scientific, nothing could be further from the truth. I firmly believe that the ever growing body of scientific knowledge is one of humankind’s greatest achievements, if not its greatest. From our unpromising vantage point on an unremarkable little planet in our equally common-all-garden galaxy we have been able to grasp many of the essential truths about the whole Universe from the incomprehensibly gigantic to the most infinitesimal constituent of a sub-atomic particle. However, it seems that many people do not fully embrace the grandeur of our achievements, or indeed in many cases the unexpected beauty and harmony that they have revealed [8]. It is to the task of understanding this viewpoint that I am addressing my thoughts.
More recently, the austerity that has enveloped much of the developed world since the 2008 Financial Crisis has had two reinforcing impacts on science in many countries. First funding has often been cut, leading to pressure on research programmes and scientists increasingly having to make an economic case for their activities; a far cry from the 1950s. Second, income has been effectively stagnant for the vast majority of people, this means that scientific expenditure can seem something of a luxury and also fuels the anti-elite feelings cited by The Economist earlier in this article.
Into this seeming morass steps Anita Makri, “editor/writer/producer and former research scientist”. In a recent Nature article she argues that the form of science communicated in popular media leaves the public vulnerable to false certainty. I reproduce some of her comments here:
“Much of the science that the public knows about and admires imparts a sense of wonder and fun about the world, or answers big existential questions. It’s in the popularization of physics through the television programmes of physicist Brian Cox and in articles about new fossils and quirky animal behaviour on the websites of newspapers. It is sellable and familiar science: rooted in hypothesis testing, experiments and discovery.
Although this science has its place, it leaves the public […] with a different, outdated view to that of scientists of what constitutes science. People expect science to offer authoritative conclusions that correspond to the deterministic model. When there’s incomplete information, imperfect knowledge or changing advice — all part and parcel of science — its authority seems to be undermined. […] A popular conclusion of that shifting scientific ground is that experts don’t know what they’re talking about.”
After my speculations about the reasons why science is held in less esteem than once was the case, I’ll return to more prosaic matters; namely food and specifically that humble staple of many a breakfast table, toast. Food science has often fared no better than its brother disciplines. The scientific guidance issued to people wanting to eat healthily can sometimes seem to gyrate wildly. For many years fat was the source of all evil, more recently sugar has become public enemy number one. Red wine was meant to have beneficial effects on heart health, then it was meant to be injurious; I’m not quite sure what the current advice consists of. As Makri states above, when advice changes as dramatically as it can do in food science, people must begin to wonder whether the scientists really know anything at all.
So where does toast fit in? Well the governmental body charged with providing advice about food in the UK is called the Food Standards Agency. They describe their job as “using our expertise and influence so that people can trust that the food they buy and eat is safe and honest.” While the FSA do sterling work in areas such as publicly providing ratings of food hygiene for restaurants and the like, their most recent campaign is one which seems at best ill-advised and at worst another nail in the public perception of the reliability of scientific advice. Such things matter because they contribute to the way that people view science in general. If scientific advice about food is seen as unsound, surely there must be questions around scientific advice about climate change, or vaccinations.
Before I am accused of belittling the FSA’s efforts, let’s consider the campaign in question, which is called Go for Gold and encourages people to consume less acrylamide. Here is some of what the FSA has to say about the matter:
“Today, the Food Standards Agency (FSA) is launching a campaign to ‘Go for Gold’, helping people understand how to minimise exposure to a possible carcinogen called acrylamide when cooking at home.
Acrylamide is a chemical that is created when many foods, particularly starchy foods like potatoes and bread, are cooked for long periods at high temperatures, such as when baking, frying, grilling, toasting and roasting. The scientific consensus is that acrylamide has the potential to cause cancer in humans.
[…]
as a general rule of thumb, aim for a golden yellow colour or lighter when frying, baking, toasting or roasting starchy foods like potatoes, root vegetables and bread.”
The Go for Gold campaign was picked up by various media outlets in the UK. For example the BBC posted an article on its web-site which opened by saying:
“Bread, chips and potatoes should be cooked to a golden yellow colour, rather than brown, to reduce our intake of a chemical which could cause cancer, government food scientists are warning.”
The BBC has been obsessed with neutrality on all subjects recently [9], but in this case they did insert the reasonable counterpoint that:
“However, Cancer Research UK [10] said the link was not proven in humans.”
Acrylamide is certainly a nasty chemical. Amongst other things, it is used in polyacrylamide gel electrophoresis, a technique used in biochemistry. If biochemists mix and pour their own gels, they have to monitor their exposure and there are time-based and lifetime limits as to how often they can do such procedures [11]. Acrylamide has also been shown to lead to cancer in mice. So what could be more reasonable that the FSA’s advice?
Food Safety – A Statistical / Risk Based Approach
Earlier I introduced Anita Makri, it is time to meet our second protagonist, David Spiegelhalter, Winton Professor for the Public Understanding of Risk in the Statistical Laboratory, Centre for Mathematical Sciences, University of Cambridge [12]. Professor Spiegelhalter has penned a response to the FSA’s Go for Gold campaign. I feel that this merits reading in entirety, but here are some highlights:
“Very high doses [of Acrylamide] have been shown to increase the risk of mice getting cancer. The IARC (International Agency for Research on Cancer) considers it a ‘probable human carcinogen’, putting it in the same category as many chemicals, red meat, being a hairdresser and shift-work.
However, there is no good evidence of harm from humans consuming acrylamide in their diet: Cancer Research UK say that ‘At the moment, there is no strong evidence linking acrylamide and cancer.’
This is not for want of trying. A massive report from the European Food Standards Agency (EFSA) lists 16 studies and 36 publications, but concludes
‘In the epidemiological studies available to date, AA intake was not associated with an increased risk of most common cancers, including those of the GI or respiratory tract, breast, prostate and bladder. A few studies suggested an increased risk for renal cell, and endometrial (in particular in never-smokers) and ovarian cancer, but the evidence is limited and inconsistent. Moreover, one study suggested a lower survival in non-smoking women with breast cancer with a high pre-diagnostic exposure to AA but more studies are necessary to confirm this result. (p185)’
[…]
[Based on the EFSA study] adults with the highest consumption of acrylamide could consume 160 times as much and still only be at a level that toxicologists think unlikely to cause increased tumours in mice.
[…]
This all seems rather reassuring, and may explain why it’s been so difficult to observe any effect of acrylamide in diet.”
Indeed, Professor Spiegelhalter, an esteemed statistician, also points out that most studies will adopt the standard criteria for statistical significance. Given that such significance levels are often set at 5%, then this means that:
“[As] each study is testing an association with a long list of cancers […], we would expect 1 in 20 of these associations to be positive by chance alone.”
He closes his article by stating – not unreasonably – that the FSA’s time and attention might be better spent on areas where causality between an agent and morbidity is well-established, for example obesity. My assumption is that the FSA has a limited budget and has to pick and choose what food issues to weigh in on. Even if we accept for the moment that there is some slight chance of a causal link between the consumption of low levels of acrylamide and cancer, there are plenty of other areas in which causality is firmly established; obesity as mentioned by Professor Spiegelhalter, excessive use of alcohol, even basic kitchen hygiene. It is hard to understand why the FSA did not put more effort into these and instead focussed on an area where the balance of scientific judgement is that there is unlikely to be an issue.
Having a mathematical background perhaps biases me, but I tend to side with Professor Spiegelhalter’s point of view. I don’t want to lay the entire blame for the poor view that some people have of science at the FSA’s door, but I don’t think campaigns like Go for Gold help very much either. The apocryphal rational man or woman will probably deduce that there is not an epidemic of acrylamide poisoning in progress. This means that they may question what the experts at the FSA are going on about. In turn this reduces respect for other – perhaps more urgent – warnings about food and drink. Such a reaction is also likely to colour how the same rational person thinks about “expert” advice in general. All of this can contribute to further cracks appearing in the public edifice of science, an outcome I find very unfortunate.
So what is to be done?
A Call for a New and More Honest Approach to Science Communications
As promised I’ll return to Anita Makri’s thoughts in the same article referenced above:
“It’s more difficult to talk about science that’s inconclusive, ambivalent, incremental and even political — it requires a shift in thinking and it does carry risks. If not communicated carefully, the idea that scientists sometimes ‘don’t know’ can open the door to those who want to contest evidence.
[…]
Scientists can influence what’s being presented by articulating how this kind of science works when they talk to journalists, or when they advise on policy and communication projects. It’s difficult to do, because it challenges the position of science as a singular guide to decision making, and because it involves owning up to not having all of the answers all the time while still maintaining a sense of authority. But done carefully, transparency will help more than harm. It will aid the restoration of trust, and clarify the role of science as a guide.”
The scientific method is meant to be about honesty. You record what you see, not what you want to see. If the data don’t support your hypothesis, you discard or amend your hypothesis. The peer-review process is meant to hold scientists to the highest levels of integrity. What Makri seems to be suggesting is for scientists to turn their lenses on themselves and how they communicate their work. Being honest where there is doubt may be scary, but not as scary as being caught out pushing certainty where no certainty is currently to be had.
Epilogue
At the beginning of this article, I promised that I would bring things back to a business context. With lots of people with PhDs in numerate sciences now plying their trade as data scientists and the like, there is an attempt to make commerce more scientific [13]. Understandably, the average member of a company will have less of an appreciation of statistics and statistical methods than their data scientists do. This can lead to data science seeming like magic; the philosopher’s stone [14]. There are obvious parallels here with how Physicists were seen in the period immediately after the Second World War.
Earlier in the text, I mused about what factors may have led to a deterioration in how the public views science and scientists. I think that there is much to be learnt from the issues I have covered in this article. If data scientists begin to try to peddle absolute truth and perfect insight (both of which, it is fair to add, are often expected from them by non-experts), as opposed to ranges of outcomes and probabilities, then the same decline in reputation probably awaits them. Instead it would be better if data scientists heeded Anita Makri’s words and tried to always be honest about what they don’t know as well as what they do.
Notes
[1]
Save to note that there really is no argument in scientific circles.
As ever Randall Munroe makes the point pithily in his Earth Temperature Timeline – https://xkcd.com/1732/.
For a primer on the area, you could do worse than watching The Royal Society‘s video:
[2]
For the record, my daughter has had every vaccine known to the UK and US health systems and I’ve had a bunch of them recently as well.
[3]
Most scientists I know would be astonished that they are considered part of the amorphous, ill-defined and obviously malevolent global “elite”. Then “elite” is just one more proxy for “the other” something which it is not popular to be in various places in the world at present.
[4]
Or what passed for debate in these post-truth times.
[5]
Mr Gove studied English at Lady Margaret Hall, Oxford, where he was also President of the Oxford Union. Clearly Oxford produces less experts than it used to in previous eras.
[6]
One that is also probably wildly inaccurate and certainly incomplete.
[7]
So Newton’s celebrated theory of gravitation is “wrong” but actually works perfectly well in most circumstances. The the Rutherford–Bohr model, where atoms are little Solar Systems, with the nucleus circled by electrons much as the planets circle the Sun is “wrong”, but actually does serve to explain a number of things; if sadly not the orbital angular momentum of electrons.
[8]
Someone should really write a book about that – watch this space!
[9]
Not least in the aforementioned EU Referendum where it felt the need to follow the views of the vast majority of economists with those of the tiny minority, implying that the same weight be attached to both points of view. For example, 99.9999% of people believe the world to be round, but in the interests of balance my mate Jim reckons it is flat.
[10]
According to their web-site: “the world’s leading charity dedicated to beating cancer through research”.
[11]
As attested to personally by the only proper scientist in our family.
[12]
Unlike Oxford (according to Mr Gove anyway), Cambridge clearly still aspires to creating experts.
[13]
By this I mean proper science and not pseudo-science like management theory and the like.
[14]
In the original, non-J.K. Rowling sense of the phrase.
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