It Really Isn’t So
How many times have you read or heard that people remember 10% of what they read, 20% of what they see, 30% of what they hear, 50% of what they see and hear, etc. The theory is rampant in the training field. I’ve read it in the literature and seen it on many Powerpoint presentations, including several of ASAE & the Center’s. Well, get this: the information is bogus. This is according to Dr. Will Thalheimer, President of Work-Learning Research Inc., whose goal is to provide research-based information to the training and learning community. On his blog, he outlines his search for the source of the data (which appears to be non-existent, by the way).
Dr. Thalheimer is not suggesting that learners don’t benefit from multi-faceted or collaborative learning. He is simply pointing out that the percentages of retention by learning type that we have all assumed to be truth are, in fact, fiction. Just goes to show that you can’t believe everything you’ve seen, or heard, or seen and heard”¦
Makes me wonder what other false “truths” are being perpetuated in our community. How often do we just trust the information we hear? Should we?
This makes me think of the conversations I’ve heard lately about the 7 Measures project. There has been criticism that the research did not include small associations, and the question posed about whether, then, the results can be applied to them. One of the research leads publicly commented that
“our findings are very consistent with the literature on systems and learning organizations. Most of the organizations with which I work in my consulting practice have budgets of $3,000,000 or less. My experience with such associations and my understanding of systems research tells me that the principles that make large organizations remarkable holds true for smaller organizations. ” In the presentations I’ve heard on the 7 Measures, they are being advocated for associations of all sizes. Is there harm in that? Not likely, since the measures are sound business principles. But, it is important for us to distinguish between what the data actually supported and what it didn’t.





