Significant Figures and the Age of the Universe

(Note: This post originally contained a remarkably stupid error in an example. For some idiotic reason, I calculated as if a liter was a cubic meter. Which, duh, it isn’t. so I was off by a factor of 1000. Pathetic, I know. Thanks to the multiple readers who pointed it out!)

The other day, I got a question via email that involves significant figures. Sigfigs are really important in things that apply math to real-world measurements. But they’re poorly understood at best by most people. I’ve written about them before, but not in a while, and this question does have a somewhat different spin on it.

Here’s the email that I got:

Do you have strong credentials in math and/or science? I am looking for someone to give an expert opinion on what seems like a simple question that requires only a short answer.

Could the matter of significant figures be relevant to an estimate changing from 20 to less than 15? What if it were 20 billion and 13.7 billion?

If the context matters, in the 80s the age of the universe was given as probably 20 billion years, maybe more. After a number of changes it is now considered to be 13.7 billion years. I believe the change was due to distinct new discoveries, but I’ve been told it was simply a matter of increasing accuracy and I need to learn about significant figures. From what I know (or think I know?) of significant figures, they don’t really come into play in this case.

The subject of significant digits is near and dear to my heart. My father was a physicist who worked as an electrical engineer producing power circuitry for military and satellite applications. I’ve talked about him before: most of the math and science that I learned before college, I learned from him. One of his pet peeves was people screwing around with numbers in ways that made no sense. One of the most common ones of that involves significant digits. He used to get really angry at people who did things with calculators, and just read off all of the digits.

He used to get really upset when people did things like, say, measure a plate with a 6 inch diameter, and say that it had an are] of 28.27433375 square inches. That’s ridiculous! If you measured a plate’s diameter to within 1/16th of an inch, you can’t use that measurement to compute its area down to less than one billionth of a square inch!

Before we really look at how to answer the question that set this off, let’s start with a quick review of what significant figures are and why they matter.

When we’re doing science, a lot of what we’re doing involves working with measurements. Whether it’s cosmologists trying to measure the age of the universe, chemists trying to measure the energy produced by a reaction, or engineers trying to measure the strength of a metal rod, science involves measurements.

Measurements are limited by the accuracy of the way we take the measurement. In the real world, there’s no such thing as a perfect measurement: all measurements are approximations. Whatever method we chose for taking a measurement of something, the measurement is accurate only to within some margin.

If I measure a plate with a ruler, I’m limited by factors like how well I can align the ruler with the edge of the plate, by what units are marked on the ruler, and by how precisely the units are marked on the ruler.

Once I’ve taken a measurement and I want to use it for a calculation, the accuracy of anything I calculate is limited by the accuracy of the measurements: the accuracy of our measurements necessarily limits the accuracy of anything we can compute from those measurements.

For a trivial example: if I want to know the total mass of the water in a tank, I can start by saying that the mass of a liter of water is one kilogram. To figure out the mass of the total volume of water in the tank, I need to know its volume. Assuming that the tank edges are all perfect right angles, and that it’s uniform depth, I can measure the depth of the water, and the length and breadth of the tank, and use those to compute the volume.

Let’s say that the tank is 512 centimeters long, and 203 centimeters wide. I measure the depth – but that’s difficult, because the water moves. I come up with it being roughly 1 meter deep – so 100 centimeters.

The volume of the tank can be computed from those figures: 5.12 times 2.03 times 1.00, or 10,393.6 liters.

Can I really conclude that the volume of the tank is 10,393.6 liters? No. Because my measurement of the depth wasn’t accurate enough. It could easily have been anything from, say, 95 centimeters to 105 centimeters, so the actual volume could range between around 9900 liters and 11000 liters. From the accuracy of my measurements, claiming that I know the volume down to a milliliter is ridiculous, when my measurement of the depth was only accurate within a range of +/- 5 centimeters!

Ideally, I might want to know a strong estimate on the bounds of the accuracy of a computation based on measurements. I can compute that if I know the measurement error bounds on each error measurement, and I can track them through the computation and come up with a good estimate of the bounds – that’s basically what I did up above, to conclude that the volume of the tank was between 9,900 and 11,000 liters. The problem with that is that we often don’t really know the precise error bounds – so even our estimate of error is an imprecise figure! And even if we did know precise error bounds, the computation becomes much more difficult when you want to track error bounds through it. (And that’s not even considering the fact that our error bounds are only another measured estimate with its own error bounds!)

Significant figures are a simple statistical tool that we can use to determine a reasonable way of estimating how much accuracy we have in our measurements, and how much accuracy we can have at the end of a computation. It’s not perfect, but most of the time, it’s good enough, and it’s really easy.

The basic concept of significant figures is simple. You count how many digits of accuracy each measurement has. The result of the computation over the measurements is accurate to the smallest number of digits of any of the measurements used in the computation.

In the water tank example, we had three significant figures of accuracy on the length and width of the tank. But we only had one significant figure on the accuracy of the depth. So we can only have one significant figure in the accuracy of the volume. So we conclude that we can say it was around 10 liters, and we can’t really say anything more precise than that. The exact value likely falls somewhere within a bell curve centered around 10 liters.

Returning to the original question: can significant figures change an estimate of the age of the universe from 20 to 13.7?

Intuitively, it might seem like it shouldn’t: sigfigs are really an extension of the idea of rounding, and 13.7 rounded to one sigfig should round down to 10, not up to 20.

I can’t say anything about the specifics of the computations that produced the estimates of 20 and 13.7 billion years. I don’t know the specific measurements or computations that were involved in that estimate.

What I can do is just work through a simple exercise in computations with significant figures to see whether it’s possible that changing the number of significant digits in a measurement could produce a change from 20 to 13.7.

So, we’re looking at two different computations that are estimating the same quantity. The first, 20, has just one significant figure. The second, 13.7 has three significant digits. What that means is that for the original computation, one of the quantities was known only to one significant figure. We can’t say whether all of the elements of the computation were limited to one sigfig, but we know at least one of them was.

So if the change from 20 to 13.7 was caused by significant digits, it means that by increasing the precision of just one element of the computation, we could produce a large change in the computed value. Let’s make it simpler, and see if we can see what’s going on by just adding one significant digit to one measurement.

Again, to keep things simple, let’s imagine that we’re doing a really simple calculation. We’ll use just two measurements x and y, and the value that we want to compute is just their product, x \times y.

Initially, we’ll say that we measured the value of x to be 8.2 – that’s a measurement with two significant figures. We measure y to be 2 – just one significant figure. The product x\times y = 8.2 \times 2 = 16.4. Then we need to reduce that product to just one significant figure, which gives us 20.

After a few years pass, and our ability to measure y gets much better: now we can measure it to two significant figures, with a new value of 1.7. Our new measurement is completely compatible with the old one – 1.7 reduced to 1 significant figure is 2.

Now we’ve got equal precision on both of the measurements – they’re now both 2 significant figures. So we can compute a new, better estimate by multiplying them together, and reducing the solution to 2 significant figures.

We multiply 8.2 by 1.7, giving us around 13.94. Reduced to 2 significant figures, that’s 14.

Adding one significant digit to just one of our measurements changed our estimate of the figure from 20 to 14.

Returning to the intuition: It seems like 14 vs 20 is a very big difference: it’s a 30 percent change from 20 to 14! Our intuition is that it’s too big a difference to be explained just by a tiny one-digit change in the precision of our measurements!

There’s two phenomena going on here that make it look so strange.

The first is that significant figures are an absolute error measurement. If I’m measuring something in inches, the difference between 15 and 20 inches is the same size error as the difference between 90 and 95 inches. If a measurement error changed a value from 90 to 84, we wouldn’t give it a second thought; but because it reduced 20 to 14, that seems worse, even though the absolute magnitude of the difference considered in the units that we’re measuring is exactly the same.

The second (and far more important one) is that a measurement of just one significant digit is a very imprecise measurement, and so any estimate that you produce from it is a very imprecise estimate. It seems like a big difference, and it is – but that’s to be expected when you try to compute a value from a very rough measurement. Off by one digit in the least significant position is usually not a big deal. But if there’s only one significant digit, then you’ve got very little precision: it’s saying that you can barely measure it. So of course adding precision is going to have a significant impact: you’re adding a lot of extra information in your increase in precision!