Multiple Working Hypotheses

In exploring Arctic ice minima I was not so much trying to reach conclusions as to find hypotheses for further testing and exploration.  Let's pick up the hypotheses side now, as I think it gets much too little attention in science education and science student practice.  In saying that, I'm projecting my bias, of course.

Part of that bias comes from having read and agreed with T. C. Chamberlin's Method of Multiple Hypotheses (1890).  Or at least liked my take on it.  It also has some correspondence to John Stuart Mill's ideas in On Liberty about a marketplace of ideas (1859), which I also liked.  The crux is, if we consider only one idea/hypothesis we are liable to be overly protective of it, or overly hostile to it.  Either way, we do not arrive at the best hypothesis for continued work.  Chances of us having started by selecting the best of all possible hypotheses, out of the infinity which could be generated, are essentially zero.

So, instead of starting with:

• Observe
• Make a hypothesis about those observations
• Make a prediction from that hypothesis
• Run an experiment to test the hypothesis

We try something more like:

• Observe
• Make multiple hypotheses that explain the observations
• Examine the hypotheses for how/where/when they lead to different predictions
• Run an experiment to distinguish between stronger and weaker hypotheses

A different take, or at least a different discussion, of the method of multiple working hypotheses is by L. Bruce Railsback

So let's apply the method to my prior post.  (Certainly there can be better examples, and I'd be delighted to hear from a teacher about their own example!  Please do comment, with links if you have examples)

I come up with a few years which seem to be of extra interest to try to understand:

• 1996 (particularly high year)
• 2007, 2012 (particularly low years)

And there are some trend lines or periods which seem to be interesting:

• 1979-2006
• 1979-1996
• 1997-2006
• 2007-2013

I also suggested that maybe there was more variability in 2007-2013 than earlier, but kind of rejected that on the basis of eyeball evaluation.  Well, eyeballs plus a pretty casual examination of the numbers.  Let's keep that one in mind.

What results is not as neat as we're often told (not a knock on the teachers -- you have to start somewhere, and it's a good idea to start with something simpler rather than something more complex).  We've got some different categories of result, or conclusions, or ... whatever you want to call them:

• Go back and look at the meteorology and oceanography of 1996, 2007, 2012 to see why (or whether those things contributed) they were unusual years for Arctic sea ice.
• There are 4 (6, well, many) trend lines to consider -- 
• how did they each perform in estimating the ice cover for 2014? (we can't control the earth, so we have to wait for nature to tell us what the result of the experiment is)
• can we reject any of them? (is the observation too far outside what could happen due to weather's usual changing?)
• Which ones can we reject?  How confidently?
• Is there any change in variability?  (Has somebody already done research on the topic?)

Plus, from the comments (EvanH:

• Is the change in area/extent being caused by changes in ice thickness?

This one points to a value of  having more than one person involved in generating the multiple hypotheses.

As we go forward, also keep in mind the comment from MMM

I don't know… 2007-14 is such a short time period, calling it a "new normal" just because there's no trend, rather than assuming it is a continuation of a declining trend where 2007 jumped ahead of the curve… seems unlikely. Not impossible, but I'd want a physical reasoning rather than just eyeballing and random trend fitting…

2007-14 really is a short period.  Remember that I observed we want more like 30 years to find a climate trend.  7 years is pretty short!  So, in pursuing those prior hypotheses, we should keep in mind the concern that there's just not enough data.  Or, at least not enough to make conclusions based _only_ on those data.  Keep looking for physical processes.

This is a different reason to have more than one person involved.  Again, we all tend to be too fond of our own hypotheses.  It is helpful to have someone else in the group to point out the weaknesses, or additional questions that need to be answered, from or for our hypotheses.