Inductive vs Deductive approaches #statisticalThinking @muralCo #imagine2020

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In this past month I've had a bunch of interesting interactions with strategists, consultants and statisticians. Quite tongue-in-cheek, but I thought I'd share this insight on #inductive vs. #deductive approaches to statistics :



Inductive vs Deductive

But more seriously, what does it mean to be inductive or deductive? I'm sharing my notes here, because it was a chance for me to experiment with the Mural service, having attended some of Mural #Imagine2020.

I'm planning to use #Mural on my next data science project and I can quickly see how this can be a great facilitation tool to work with problem owners and business leaders to bring design thinking and a clear analytical thought process to their ill-defined business problems.


Figure 1: Inductive

Let's have a hypothetical situation of a manufacturer of unique personal mobility devices. It's high quality, engineered product and battery-powered device that transport users through urban areas. Average retail price of USD$1,000 per unit. Think: electronic skateboard.

Here, we found out that there is an increasing trend of customer complaints. Customers are finding that the product doesn't meet advertised specifications and are unsatisfied with their purchases. Conversely, other competitors have been increasing market share by selling more products and have been stealing a march against us. In other investigations, we've discovered that the operations team runs out of important components in stock. 

This kind of inductive "bottom-up" approach (read from the left to the right) to derive a problem statement is great when you know something about the right hand side of the Mural. But you're still trying to understand the relationship between the moving parts.

Figure 2: Deductive

Same situation - this time we start with the overall problem statement and disaggregate it into its component parts. We use this when we have a very clear idea of the problem structure. Especially when you are using a mathematical relationship of a framework: Economic Value Added, Return on Capital Employed, etc.

In this case, you might see that once we disaggregated the overall problem, we've quickly honed in on the levers and identified some root causes. At this point we might pivot out and focus our efforts on confirming a new hypothesis: "Our electronic skateboards aren't price-competitive and aren't reliable because of poorly engineered and expensive motor components".




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