Good to Great, or Just Good?

The Good to Great book may have some very good points about what makes a company a good company but some of the claims aren’t based on great evidence.  In the book Collins used stock returns and his idea of outperforming companies in like industries in order to decide who was the elite firms that “made the leap” to greatness.  He found eleven firms who had achieved this.

There is many problems with how he came to his conclusions.  For one, Collins even realized this fact, these two traits that were used to show greatness doesn’t necessarily relate to other companies and might not necessarily work for other companies.  Another major problem with how he came to his conclusions is that he used data mining.  Data mining is a very skewed way to do statistics.  It is also only good for that particular sample that is being looked at, it doesn’t tell us anything prior about the data or anything relevant to the future.  For instance if a person used data mining to see where to invest their money in the stock market it would be like gambling their money.  According to Hand using data mining is basing everything on chance.

Collins also left out another important factor that must accompany data mining; that is once the results are obtained they need to be tested.  They need to be tested to see if it just a random thing that is only a characteristic of that particular sample.  The article doesn’t discredit Collins efforts completely though.  It says that his findings may be true but it argues that there is no evidence to support them.  In order to check the validity of Collins results some tests were ran to check his findings.  The tests looked at the stock return for periods longer than the sample.  They looked at the years prior, after, and all together.  The findings from these tests show that Collins “GTG” companies weren’t even close to the top in this category.  The bottom line is that Collins needed to test his results and this would have supported his claims better.

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