Science is better than facts

By | February 23, 2017

Facts have recently been all over the place. With the Trump thing and the political arguments about climate change, evolution, inauguration crowd sizes, and funding of science, perhaps it is the time to have a closer look at what facts are (see also this post).

My take:

  1. Science rarely works with facts. Surprisingly, the term fact almost never appears in peer-reviewed scientific publications. I don't hear it at seminars or conferences either. We scientists tend to avoid facts, it is not how we think about the world. Instead, we test hypotheses, compare models, build theories; we observe and we explore data; we make assumptions, assign probabilities; we falsify, reject, criticize. And we doubt. We seem reluctant to establish facts. Even in the realm of purely deductive logic (1 + 1 = 2) we rarely use the term fact – we simply describe the logical flow.
  2. Facts are definitive, science is not. Just recall Copernicus and Galilei – what was once a fact accepted by the entire academic community turned out to be a colossal mistake. Such stories are not uncommon, there have always been scientific revolutions, paradigm shifts, or just ordinary theory improvements. Science changes.On top of that, the world can be annoyingly stochastic and we have limited ability to observe it and experiment with it – hence, uncertainty creeps in. That is why we have probability theory, and why probability is so useful. Note: Even the language of law is careful not to be too definitive, and it implicitly works with probabilities: When a strong and solid evidence is presented, it proves beyond reasonable doubt, not definitively.
  3. Observations can be facts, but what about the rest of science? We can consider individual observations and measurements (the data) to be facts (see also this post). Although, there is the problem of measurement error and observational bias, this is a technical issue that can be addressed. In general, credible observations and measurements do exist, and it is reasonable to label some of them as facts. Yet observations and measurements are only the beginning of science, on their own they are of little use. To make observations useful we must use them to test hypotheses, compare models, assign probabilities, build theories, and make predictions. But speaking about facts in this hypothetical part of science is tricky.
  4. Facts are either trivial, or too radical. Let me elaborate on hypotheses and statements. Simple examples: „Evolution happens“, „Climate changes“. Since it is hard to imagine climate or living things to remain strictly constant through time, I'd put the probability of these statements being true, given all I've seen so far, to be way above 0.99999999999. In other words, I am happy to label a temporal change of almost anything as a fact, albeit a trivial one.

    Things get trickier when statements get specific. Example: „Our planet has warmed up during the last 100 years.“ Is this a fact? The first complication is the multitude of meanings that hide beyond such a simple statement. How exactly do we measure global temperature? Where? And how do we aggregate it from local measurements to the global scale? Different approaches will give different quantitative results. Second, measurements are not always precise. Yet even if I consider the multitude of meanings and the measurement errors, and given all I've read so far, I'd assign the statement probability above 0.999: This is my subjective confidence about whether the planet has warmed up. It is quite high, so perhaps I might be tempted to speak about a fact. On the other hand, it is less of a fact than the previous statements. Also, this fact is politically irrelevant.

    How about a more relevant: „Humans have caused global warming by excessive exploitation of fossil fuels, and subsequent increased concentration of greenhouse gases in the atmosphere.“ There are surely many assumptions, definitions, interpretations and imprecisions that will influence my confidence in this statement. Given what I know (I am an ecologist, not a climate expert), I would give the statement probability of 0.95. Now is this a fact? Would 0.85 also be a fact? Personally, I'd say that labelling this statement as fact is unnecessary radical.

    Finally, here is something from the core of the current political debate: „If all countries dramatically reduce greenhouse gas emissions right now, the climate change will slow down.“ And here I have doubts, since this requires forecasting, and forecasts tend to be messy. I would lean towards thinking that the statement is somehow reasonable, so I give it probability of 0.7, yet I would definitely not label it as fact. If I were a politician, I would probably be willing to bet my career on it, but partly because I think that career choices are always a bit of a gamble, not because of a strong conviction (plus, I think that switching to clean energy could be fun since it would piss off Putin).

To summarize, science does not exactly need facts, there are always better words to be used. And in some cases science should actually avoid facts as something unhelpful or exaggerated. The language of probability is what we should use instead.

However, one argument for working with facts is that the general public doesn't care about how academics think – facts are a useful simplification for the masses, executives, and politicians. You scientists can do your smarty-pants gibberish, but you must also give us some hard facts that we can work with.

Thoughts on that: Asking scientists to provide facts is just shifting the responsibility for decisions from executives and politicians to scientists. Second, I doubt that labelling arguments as scientific facts makes them more persuasive – a discussion where one side claims to own the facts is, unfortunately, prone to end up as a fight. It is better to persuade with logos, ethos, and pathos, rather than with labels. Third, demagogues, ideologues and populists have their alternative facts; a considerable part of global population is willing to kill for all kinds of random bullshit, that is how certain they are about their facts. Feeding them stuff labelled as scientific facts is like pouring gasoline to fire.

I suggest a solution, although a time consuming one: We need to serve scientific method, critical thinking, and probability theory to masses in smaller doses, from an early age. If pupils are able to get algebra and reading, they are able to get probability. If high school students are able to read Shakespeare, they are able to understand what a peer-reviewed article is. Scientific method must be put on the same level as scientific findings, languages, history, math or literature. Scientist must do more to popularize not only their results (or facts), but also the way they work and think. It is the only way how the general public, and subsequently the politicians and decision makers, will ever take us seriously.

PS (27/2/2017): In response to this post, Andy Gonzales pointed me to this article in The New Yorker.

PPS (27/2/2017): Uncertainty does not end with probability. There can actually be uncertainty about the probability itself -- we can put credible intervals around probability: I can say that probability of something is 0.8, plus minus 0.1.


3 thoughts on “Science is better than facts

  1. Claudia Garrido

    Thank you for this post. I totally agree that we need to explain better the scientific method to non-scientists. The problem may be that it is hard to stand uncertainty. Many people want to know the "absolute truth" and doubt of science when it doesn't provide this security. As you say, the solution is time consuming. But more than worth it in my opinion.

    1. Petr Keil Post author

      Hi Claudia, I would even go farther: Communication (and appreciation) of uncertainty is perhaps one of the greatest challenges of current science.

  2. hentaihavne

    Lead author Petr Keil of the German Centre for Integrative Biodiversity Research, noted that current methods only consider the amount of area lost, rather than where it is lost, when estimating species extinctions.


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