Quo Vadis Training & Consulting

Two weeks ago I wrote about an interesting experiment in the Wall Street Journal to compare intuition versus objectivity in picking winners in this year’s NCAA men’s basketball tournament. The intuitive method was straightforward – pick the winner of each game using your instincts, biases, likes and dislikes. The objective method designed by the Journal required you to predict winners knowing only certain information about the teams (e.g., experience, height, 3 point shooting ability, etc.), but not the specific identity of the teams. I decided to play along.

So, two weeks and sixty games later, how did I do? Or rather, how did my instincts do versus my objectivity? Drum roll, please…

In those games where the methods resulted in my predicting different winners, I picked the correct outcome four times using my instincts versus three times using objectivity.

What conclusions can we draw? Does instinct trump objectivity? Hardly.

The first thing that jumped out – which I noticed before a single game had been played – was the extremely limited sample size. I predicted different winners in only seven games!

So, was there anything unique about those seven games that might prove enlightening? As a matter of fact, yes: all seven of those games involved teams seeded between five and twelve. That is, they didn’t involve any top four seeds (and thus no bottom four seeds). And it’s important to note that while I didn’t know the identity of the teams using the Journal’s method, I did know the team’s approximate seeding (the Journal gave the team’s seeding within a range of two (i.e., “a 1 or 2 seed,” “a 5 or 6 seed,” etc.). Of the 32 first-round games, I picked the lower seed to win only twice by instinct and only four times by objectivity. Clearly, seeding heavily influenced my selections in both methods, and thus this really wasn’t a comparison of intuition versus objectivity.

There’s much more we could explore in this comparison, but in the interest of brevity I’ll end here with the following thought: intuition and objectivity are extremely difficult, if not impossible, to separate. Intuition is the interpretation of our past experiences (including lots of factual data). Objectivity often requires that we choose among competing measures and sources of data, which we can attempt to do scientifically, but often times we do because something feels better.

The debate of objectivity versus intuition, while interesting, really isn’t an issue of either or. And in designing experiments to explore it, we need to be especially mindful of how the two are often entangled.

It’s mid-March, which of course means the NCAA Basketball tournament and the detailed analysis that drives our bracket predictions.

Uh, wait….detailed analysis?

Do we really analyze the teams and matchups in forecasting the outcome of the tournament? Or do we rely on our instincts and simply guess? Do we even know how much of each is involved in our selections? Most important, which is better – instinct or data-driven analysis?

I found a fun way yesterday for us to test ourselves, at least in the case of this year’s NCAA tournament. Fill out your bracket by your traditional method (presumably relying heavily on instinct?), then go to WSJ.com/sports and complete their “Blindfold Brackets.” The Journal has replaced each tournament team name with a random inanimate object (e.g., “The Quilts”) and provided a number of characteristics that describe the team (e.g., the team’s experience, height, three point shooting, etc.) without giving away its identity. You pick the winner of each matchup based on those characteristics. The process isn’t perfect (are the Journal’s characteristics the best predictors of the outcome of a game? Is their assessment of each team on each characteristic accurate and differentiating?), but it’s intriguing enough for me to test it.

At the conclusion of the tournament I’ll compare my traditional and blind brackets to see where I did best and share my findings in another blog post.

Maybe experiments like this will demonstrate that objectivity really does beat intuition? Maybe then we might start seriously using objective methods in our everyday professional decision making. Maybe we’d look not at the name, but rather the defining characteristics of different companies that we decided to pursue as business development prospects? Maybe then we’d do the same as we evaluated and promoted our employees? Or in deciding among investment options? Or projects to pursue?

Maybe.

It’s been an embarrassingly long time since I last posted.

For me, blogging is an important routine, or habit, I try to maintain, just like exercising, healthy eating, doing crossword puzzles and reading the daily business press. These are activities that provide long-term but few short-term benefits, at least not many that are measurable. And as a result, it’s easy to “fall off the wagon,” to set aside these commitments to tend to short-term emergencies, indulge in other distractions, or just enjoy some personal down time.

But these sabbaticals, if you will, aren’t free – when I don’t blog, I lose connection with my network; when I skip my crossword puzzles, my thinking softens; when I don’t read the paper, I’m less aware, less curious, and may miss business opportunities; when I don’t exercise or eat right, ….. you get the idea.

What’s worse, it’s often hard to restart our good habits – “I’ll start my diet next week,” “I don’t have time to do the crossword today,” “I don’t have anything worth blogging about.”

We all have our own long-term goals. Typically their achievement depends to a large degree on our steady, long-term commitment to some set of small, regular activities. Skipping them, here or there, once in a while, doesn’t seem to matter much, until the skipping becomes the routine. Understanding the connection between our long-term goals and short-term habits helps us stay on a steady course, and achieve our goals.

Anyone for an early morning run tomorrow?

In each of my last two posts, I showed how histograms help identify distinct subgroups in our data, focus our analysis, and produce more powerful findings. I used real-world examples, but made-up data.

Why?

Because real-world data is messy, often full of random noise, and with odd proportions, making it less-than-ideal for illustration purposes. This is not a limitation of the histogram, but rather a challenge for us as we use them.

Consider the following example exploring the frequency of a product’s weekly sales.

As you can see, our data breaks cleanly into two distinct subgroups – weeks when we advertised the product, and weeks when we didn’t. As you’d expect, we sell more when we advertise – in fact, a lot more, making it easy to see these two subgroups.

But what if our advertising was less effective? And what if our product had a higher base sales level? The two subgroups still exist, but they’re less distinct, as in the following histogram.

Now let’s assume there was greater variance in weekly sales, due to any number of factors: volatile consumer demand, product stock-outs, uncertain economic conditions, etc. Our weekly sales would be less consistent and distributed more broadly as below (note: this would be true both when we advertise and when we don’t).

What happens when we look at the data not as two subgroups, but as one population? Our two subgroups add together to create the following overall sales distribution (the red line).

Hey, wait a minute! This looks like a homogenous, normally distributed set of data!!

It’s important to note, in the real world, THIS is the view we’d start with. We only get to the more revealing views of sales with and without advertising, when we imagine the differences, sort the data, and run a histogram to compare the two subgroups. Looking at the above combined view, it’s easy to see how this can be missed.

The histogram is a great analytical tool. But like most tools, batteries aren’t included – tools don’t work on their own. Thinking on our part gives it its power.