All ravens are black, except when they are not


In previous post Snow, a thing of the past I explored the positions of both sides of the debate concerning the connection between snow and global warming. One thing I didn’t touch yet was the skeptics’ remark that “One can not have it both”. There were statements before that snow was a thing of the past because of global warming, but now when snow falls in abundance, there is the statement this is also because of global warming. If this is the case, it can not be proven right nor wrong.

By their nature, skeptics are, well… skeptical. They want testable statements that can prove or disprove something. This is rooted in science and is called falsifiability. In the most simplest way it looks like this: a hypothesis (testable statement) must predict at least one observation by which it can be refuted. If the evidence is in line with the prediction, the statement may be right. If the evidence conflicts with the prediction, the statement is wrong.

Maybe a bit theoretical, so an example to make it clear. Let’s suppose the statement: “All ravens are black”. It can be made testable by stating that if all ravens are black, this means no raven with another color will exist.

  • If we find a white raven, then the statement “All ravens are black” is clearly wrong: it is falsified (proven to be false)
  • If we see only black ravens, then the statement might be right. “Might” because there is always the possibility that we just didn’t find the ones with another color. They could exist, even if we didn’t find them (yet).

By the way, white ravens do exist (although they are rare).

This is a very simple example and it should be nice if this was also applicable to global warming. The reality is not always that simple. Predicting something is hard, especially about the future. Especially about a complex thing like the climate. So statements about global warming will be stated in a less clear manner. Something like: “it will be more likely”, “it might” or “it is unlikely”. These are all stated as a probability.

One problem: statements based on probability are not falsifiable by default. This is not difficult to understand: suppose one has the statement It is very likely that the frequency of heavy precipitation events will increase. Suppose we define very likely as more than 90% chance. What happens when we test the precipitation in certain places over a certain time?

  • If we find an increase in heavy precipitation, the statement might be true.
  • If we find a decrease in heavy precipitation, the statement isn’t necessarily false. There is a possibility of at least 90% of an increase, but this also means a possibility of up to 10% equal or a decrease in heavy precipitation. It might even be possible that the precipitation was measured in a different place where the increase was not observed. Or maybe the time frame wasn’t long enough to be able to see the increase.

In conclusion, even if the outcome is not consistent with the statement, it doesn’t falsify it.

Of course, first the meaning of increase in frequency and heavy precipitation and probably also the time frame have to be defined. If not, the statement is almost meaningless and one could prove about everything.

This seems disturbing. Does this means that many of the global warming statements can’t even be falsified? I was puzzled about this for a long time. Now I think it is not necessarily true. In the real world we realize that falsifiability is important, but not the only thing being taken into consideration.

Suppose a probability statement that eventually fails. As we saw this doesn’t mean the statement is false, but it doesn’t exactly add to the credibility of the statement and those who make it. The higher the probability with which the statement was stated, the more credibility loss. It is not surprising at all to see fading confidence if observations are not in line with what one would expect from the theory.

A non falsifiable statement looks like a horrible thing when trying to prove or disprove something. But look at it from the other side: a probability statement has, well…probability. Snow and heavy precipitation are consistent with the theory of global warming, but they are also consistent with other things. There isn’t necessarily just one theory. By the fact the theory has to be proved by probability statements, there will be at least one competing theory, maybe even more. All with their own probabilities, explanation of previous observations and predictive value.

This post started with the remark “One can not have it both”. Well, can it? Of course not. At least not in the sense of piling up statements so a theory will be confirmed with any observation possible. That would be the same as stating that “All ravens are black, except when they are not”. This doesn’t learn us nothing new, the predictive value of this is exactly zero.

It is easy to come with an explanation after the facts. The real test is how a theory predicts data we haven’t seen yet and also describes previous and current observations best. This doesn’t necessarily means falsification in a strict true|false way as we would expect with the “One can not have it both”-remark. But rather a comparison between competing theories.


6 thoughts on “All ravens are black, except when they are not

  1. eSell

    I really liked this one–it explains very clearly that in order for a scientific theory to be valid (or for a hypothesis to become a theory) it must be testable and it must pass the tests–or it must be in some manner predictive…and then the prediction must work out.

    If the GW example is right–“snow is a thing of the past” and then later “an increase in snow is consistent with the GW theory/hypothesis” then someone is saying something wrong somewhere. Or it is a half-baked hypothesis.


  2. trustyetverify Post author

    Thanks for the like and for sharing your thoughts about this, eSell.

    Indeed, in order to be valid, a theory must be testable and must pass the test. The added difficulty specifically with global warming however is that climate science is not an exact science and this makes it difficult to come up with pure, testable claims. This means that both sides of the debate mostly will make probability statements and we have hypotheses that are “half-baken”. Luckily not all more or less baken hypotheses have the same probability. Time and/or research will learn which one is (more) correct (than the other). But this makes the global warming/climate change debate much more complicated though. I hope this is the message that readers take home after reading this post.


    1. eSell

      Yes, it can all be very complicated. For example, I am currently reading through Cosmos by Carl Sagan–written back in 1979. He mentions Global Warming in reference to Venus, which appears to suffer from a type of runaway greenhouse effect. Sagan mentioned, however, that part of how humans change the planet is turning forests to fields and grasslands, and turning grasslands to desert. Grasslands reflect more sunlight from the surface of the earth than forests, and deserts reflect more than grasslands–something called the Albedo Effect. I haven’t actually heard this talked about much in the current Climate Debate and I wonder how much of a regulating power it might be having on GW.


  3. trustyetverify Post author

    Indeed, I also don’t hear much about the effects of changing land use on albedo. There is much more of a discussion going on about changing albedo in the Arctic or about cloud albedo. The latter is rather interesting. Denser, lower clouds have a higher albedo, higher clouds tend to trap heat. Some think clouds have a warming effect in a warming world, other thinks it has a regulating effect on temperatures. Some think the effect is small, others think it is considerable. There is still a lot to discover.


    1. eSell

      I don’t know which is most accurate, but I do know that spring has been late up here–the normal for the last 3 weeks has been 13C, but we’ve been lucky to hit 8 most days, though this upcoming Sunday is supposed to be 20, so that will be lovely. lol



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