Hey all, this is a shameless little plug for a great little organization, The Species Alliance. The organization focuses on current species extinctions, notably the growing biodiversity crisis. They’re in the process of producing a very nice film (yours truly played a very small role in its making), a trailer of which can be viewed here. Watch it. If it doesn’t change the way that you think about things, it’s because you’ve already been thinking. And, for the younger-at-heart viewers, take a look at The BioDaVersity Code. Check them out!
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I was discussing the issue of uncertainty today with a colleague, specifically the uncertainty that surrounds predictions of future climate change, and the models that generate those predictions. My colleague is concerned that when scientists use the term “uncertainty” we convey to the non-scientist public that we don’t know what we are talking about. He likened this to the use of the word “theory”, and how detractors from the scientific method, such as advocates of Intelligent Design, exploit misunderstandings of scientific terms, or the specific meanings of various words in a scientifc context.
Well, let’s explore this little issue. I will address “theory”, and then deal with “uncertainty” in a later posting. What follows is a little blurb that I wrote almost two years ago as a supporting statement for the California Academy of Sciences‘ stance on evolution. But first let me point out two things:
- In science, there is no validity to the phrase “just a theory”. A theory is IT! An idea doesn’t get any better, is never rated any higher. To use that phrase is akin to saying “Jessie Owens won just gold medals”. Until they begin to award platinum medals…
- Theories are objective, no beliefs necessary. When asked if I believe in evolution, I state flatly, NO. Evolution is an observation, not a belief. Darwin’s Theory of Evolution by Natural Selection is the leading theory that explains evolution.
Okay, the blurb:
Some of the confusion that surrounds discussions of Evolution and other scientific concepts stems from the various meanings of the word theory. “Theory” has a very specific meaning in science that is distinct from its everyday common usage. The difference is best explained by outlining the stages of scientific investigation.
Scientific inquiry often begins with the formulation of an idea to explain an observation of the natural world. Such ideas are termed hypotheses, and they are equivalent to the non-scientific usage of “theory”.
The goal of the modern scientific method is to formulate testable hypotheses, that is, ideas that can be tested directly by evidence and experimentation. Two important qualities of such tests are that they are:
Repeatable, meaning that other scientists can apply the tests objectively and independently, and
Verifiable, meaning that the results of the tests are consistent, regardless of who has conducted the tests.
Hypotheses may be subjected to many different tests by many different scientists. Often it is found that some part of the hypothesis is inconsistent with test results, in which case the hypothesis might have to be revised. In other instances, the hypothesis may be found to be wholly inconsistent with tests and observations, and it is rejected altogether. The result is that hypothesis testing is an ongoing process of formulation, testing, and revision.
When a hypothesis is found to be consistent, and holds up under extensive testing, then it is generally agreed that it represents a fundamental explanation of the observations that it was originally formulated for. In this case, the hypothesis-turned-fundamental concept is elevated to the status of a theory. It is important to note that in no case is a scientific theory free from further testing and revision, nor is it necessarily considered a sufficient explanation of the observations to the exclusion of additional testable scientific hypotheses.
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Hi all, and apologies for a long-delayed posting. I’ve been swamped with “science” work, the details of which are probably not exciting enough for a blog post! But, I’m back.
Someone raised the opinion to me the other day that a certain former Vice-President must be wrong; if we can’t predict the weather tomorrow, how can we possibly predict the climate 50 years from now? Excellent question, with two answers. First, weather is not climate. Weather describes daily atmospheric conditions; rainy, foggy, sunny (not in SF!). Climate could be described instead as a long-term average of daily weather. For example, the Californian climate is Mediterranean, meaning roughly that we have rainy winters and very dry, warm summers. So, January 2008 will have more rainfall than July 2007. But do you know which January days will be the rainy ones?
Why do we know more about the climate next year than we do about next week’s weather? That brings me to the second answer. Scale and uncertainty matter. Look at the table that your computer is resting on. Looks relatively smooth, doesn’t it? But magnify the surface 10,000 times and the landscape would probably be more reminescent of Nevada’s Great Basin. Or, roll a tennis ball down a steep street (please check for traffic first!). You have no doubt of the outcome, but can anyone predict the precise path of the ball, given the roughness of both ball and road surface, wind speed, etc.? Uncertainty at small scales very often average out at larger scales, both in space and time. The table is smooth enough for your computer, balls roll downhill, and increasing atmospheric concentrations of greenhouse gases cause global warming. Of these things we are certain.
There are small-scale components and interactions within systems, however, that can matter to the overall behaviour of larger systems. Uncertainty at these scales may generate uncertainty at larger scales. Some of you have probably heard of the anecdotal butterfly flapping its wings in China, and via the wonders of chaotic dynamics, causes a storm over the Atlantic. Well, it isn’t quite that simple, but concepts such as chaos, nonlinear dynamics, positive feedback, thresholds and tipping points, become very important when predicting Earth’s future climate. More on these next time, I promise!
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