Confirmation Bias

We often look for data to support our proposed solution. "The Drunkard's Walk: How Randomness Rules Our Lives " mentions this in relation to us picking only the random events that support our hypotheses. We also will plan experiments that support our existing hypothesis instead of looking for ways to disprove it.

The best solutions are those which can't be made to fail as opposed to those which need the stars aligned (or maybe that special operator) to work.

Psychologist's also have a term for this as I heard on the radio this morning. "Magical Thinking" or something like that. We tend only to remember what we want and not look at the data impartially.

Fly larvae and simple evidence.

In the Monster of Florence, the investigators ignored simple evidence that cannot be faked and instead relied on the testimony of people with vested interests in the outcome. Instead of taking the evidence of fly larvae on the corpses (fly larvae had no interest in the outcome of the murder investigation), they instead trusted the testimony of people since that testimony fit their theory as to the time of death better than the physical evidence of the body's decay.

When investigating a root cause and gathering evidence to support a solution, keep in mind the types of evidence you are gathering. Often we want a certain outcome to be true and interpret information to fit that outcome. This will often lead you down the wrong path. Try to gather evidence that is independent of the solution you seek. I call this simple evidence. It cannot be faked and can only be interpreted in one way.

If your evidence requires assumptions then it is not simple evidence. Those assumptions may be wrong. Often our assumptions are biased by our experiences our outlook. Try to avoid them.

Occam's Razor

I just finished, "The Monster of Florence ". Douglas Preston and Mario Spezi's account of a serial killer in Florence, Italy and the investigations attempting to find the killer(s).

I was struck by the complexity of the theories that the investigators proposed in order to build their case against some individuals (and groups). This brought to mind Occam's Razor's, a principle that states that you should make as few assumptions as possible when trying to explain a phenomenon.

This applies to finding solutions. The more complex the solution, you develop, the more chances there are for problems in the future. Keeps things simple and your solutions will have greater longevity, be easier to implement, and easier for others to follow. Complex solutions can be a house of cards that will come crashing down when one aspect or another isn't fully implemented as intended.

De Bono's Six Hats

One problem solving technique commonly used is brainstorming - a technique with which I'm sure you are all familiar. However, we all see things from our perspective. One variation to try to force you out of your "common sense" is the De Bono hats. There are many references on the web and published so you can look them up for yourself.

One thing I'm contemplating is whether you can do this within your own field. Sometimes we try to solve all problems with whatever tool we're best at. Try using a tool other than your favorite for the problem.

For example, maybe Excel isn't the best tool for presenting your data. Perhaps a Word document would be better or even - dare I say - Powerpoint.

Don't use duct tape and vise grips to fix everything. Get to learn different tools and give them a try.

Changing E-Mail Subjects

Along the lines of my earlier post about e-mailing.

Have you ever been part of an e-mail chain where the subject morphed from the original to something else?

Don't be afraid to change the subject line or recipients of an e-mail chain or delete non-relevant sections. In addition to intellectual property issues, there can be a lot of waste associated with not keeping the e-mail header information current.

E-mail is pervasive and many people get hundreds of e-mails a day. If your subject does not communicate effectively, your message won't even see the light of day.

Conditions & Actions

When doing a cause & effect analysis, remember that for any effect there is both a condition and an action that must occur.

In order for a fire to start, you need more than the conditions of fuel, oxygen and heat. You also need an action (e.g. a spark, a match strike) for the first to start. Sometimes the conditions are actions might be so obvious that you don't think of them at first, but it helps to include them since ti broadens your thinking - leading to better brainstorming or diverse thinking. You might not think to include oxygen as a condition for a fire but you might miss an important solution if you don't. That's why we use inert gas (nitrogen) glove boxes when working with pyrophoric materials.

Looking for both conditions and actions will help you to build a more complete picture and ultimately lead to more effective solutions.

Bias in Visualization

When faced with a large amount of data, one if the first things I do is graph the data in some way to get a visual impression. I'll even graph simple linear calibration plots since a quick glance will give a better impression of the data than looking at the slope, intercept and correlation coefficient. In this case, the visualization shows more than the individual data.

However, the opposite can happen, even though I'm sure you're familiar with the adage, "a picture is worth a thousand words." Recently the Flowing Data site held a visualization contest. The results were interesting. Even though everyone started with the same data set, each visualization tended to emphasize something different about the data set. As a whole, the visualizations presented a complete picture and highlighted aspects of the data one couldn't see from just the numbers, but each individual visualization tended to focus on one thing at the expense of others.

This may be your intent when visualizing data, but watch out for your own bias. Always include the data used to create your visualization (or when this is not practical a reference to it) so that others with a different perspective can visualize the data their own way and perhaps glean something different than you did.

There is fine line between illuminating data for your audience and prejudicing them.