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Why Measure Training?

Apr 23, 2018

The following article was recently featured on eLearningIndustry.com

Reasons Why You Should Measure Training

We all have limited resources and a lot of important problems to solve. We already give people some sort of credit for what they’ve done once they’ve done it. Why should we take the time and energy to try to measure the work we’ve already done?

To make a decision about the continuation of that work, or about how to do better and similar work in the future.

And only when the value of this decision is greater than the cost of making the measurement.

That’s pretty much it. For those of us in Learning and Development, there is no other justifiable use case.

Measurements of anything that happened in the past can be quite useful in reducing uncertainty about the future. For cost centers, like Training, the only reason we care about measurements in the past is when someone is actively making a decision about an outcome in the future.

If someone asked you for a report or measurement or whatever, they would probably want to decide something—and they would need your data to help them do that. This isn’t always upfront in the request, but it is inevitably there if you dig for it.

No matter how scientifically accurate our measurements may be, all we can ever do with them is reduce uncertainty about the future. There will be no guarantees. Science has been around for a long time, and it has never been in that business.

Science helps us make better guesses with the data we’ve gathered. 

It’s important to note that science does not tell us what data we need to gather, or how much of it we need to have before it counts for something. For instance, the “statistically relevant sample size” is never a set number or percentage. It is a value judgment. Your value judgment. You make the call.

How much measuring is enough?

How much data do you need?

After all, you don’t need all the data there could ever be; you only need enough of a sampling of the data to make a guess (aka “an inference”). That’s how the whole ‘science thing’ works.

The good news is, as Douglas Hubbard (inventor of Applied Information Economics) puts it: “You have more data than you think, and you need less than you think”.

What Does That Mean?

Well, the more uncertainty you have, the more benefit you get from a simple observation.

In other words, if you know almost nothing, almost anything will tell you something.

Chances are that you do know something already. You probably know quite a few things, in fact. Here, you can listen to a 30min podcast where Doug expands on this idea.

So, what do you need to make your measurement? Not much.

  • You don’t need a database
    Science brought us to databases, not the other way around.
  • You don’t need to be original
    Chances are you’re not the first person in the history of the world to want to measure this. The internet is right here at your fingertips.
  • You don’t need to account for every possibility
    Because that would be impossible. So, take a deep breath…

All You Need Is A Question And An Observation. Maybe Some Analysis, Too

Example 1…

[continue reading now on eLearningIndustry.com]

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