No measurement is absolute. Many policy decisions, however, are based on the premise of perfection in measurement.
Episode Summary:
No measurement is absolute. In fact, finding and accounting for error in measurement is frequently harder and more important than the base measurement itself. In important social and medical issues such as global warming and COVID-19, however, figures are presented as absolute and without uncertainty. No engineer would declare a critical dimension or measured attribute as absolute, but the practice is common among policy makers and mass media. That’s simply a bad idea, says Jim Anderton.
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Transcript of this week’s show:
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This simple instrument is obviously a steel rule. Take a look. How long is this steel rule? Most of us would say 6 inches, because that is the way this particular instrument is graduated—by inches and fractions of an inch, and it’s about 6 inches long. To the engineer, however, the length of the steel rule is something on the order of 6 inches, plus or minus perhaps 1/64 of an inch.
Put another way, it might be 6.00 + or – .02 or .03 inches. Nothing in manufacturing is made to print, with perfect accuracy. Every dimension has an error associated with that measurement. It does not matter whether the measurement is temperature, length, width, volume, viscosity, velocity or wavelength—if it has been measured by humans, that measurement has inbuilt error. Increasing the number of measurements allows us to use statistical methods to make inferences about the precision of a large batch of measurements, but the error is still there.
So, if this is self-evident to engineering professionals, why bring it up here? Well, mainly because the public are exposed to statistics and measurements which are widely circulated, yet which are almost never quoted with their associated uncertainty. Covid-19 is an example. Covid-19 deaths are reported as absolute, on the assumption that someone who died of Covid-19 died exclusively because of Covid-19. The reality is that there is a large grey area which includes many victims whose death was accelerated by Covid-19, but not caused exclusively by Covid-19.
That is uncertainty, an error in measurement. That error is never quoted even as those mortality statistics are widely reported around the world. Infections, too, have an error associated with them. No test is perfect, and certainty in testing has been similarly absent from the reporting. Global warming is another example. We have all seen the famed hockey-stick charts and graphical representations of the rise in atmospheric CO2.
What we never seen are the error bars marking the uncertainty in those measurements. Every first-year college student in science or engineering learns how to plot a best-fit curve through a data set that includes error bars. Is that best-fit curve an absolute determinant of truth? No, but a set of curves helps create boundary conditions that give an engineer or scientist a set of possible outcomes for any measurement. Is that close enough? If it isn’t, then you go back to your measurement and test methodology and find a better measurement technique, better equipment or both. But you never take one curve and declare it to be the absolute truth.
Everything from the mythical “years left to the point of no return,” to sea-level rise, to global CO2 concentrations have been presented with the indiscriminate use of graphical tricks like scale compression and best-fit curves to create the outcomes that the presenter wants. You can find statistics online that show that we are five years away from irrevocable species extinction. You can similarly find results that claim global warming is a hoax. Like Covid-19, the need to paper over the reality of uncertainty in measurement, methodology and statistics means that the average citizen is not actually hearing the truth about important issues like these.
The concept of uncertainty in measurement is not new, or too complicated for non-technical people to understand. Yet I do not see it in the reporting. Why? Some say that journalists are lazy; conspiracy theorists claim the reason is that they want produce results that prove what a politician wants to prove. Still others claim that the political imperative to simplify complex issues means that uncertainty in measurement has to be left out. Well, it doesn’t. So when you hear a statistic like 11 years before the carbon dioxide reaches the point of no return, or that 1,200 people died of Covid-19 last week, it is important to ask yourself: plus or minus what?
Uncertainty is not a bad thing. In engineering, it’s honesty in its purest form.
