HR loves best practices. After all, they give us an idea of what HR organizations in other companies are doing, provide insights into the positive outcomes that those practices claim to achieve, and can even provide a step-by-step approach on how to implement. Flushed with excitement, many HR organizations with good intentions can blindly adopt these practices with the hope of achieving similar results within their own firms. And although understanding “best practices” is a good way to gain insights into what others are doing and to use that information to generate ideas and inspiration for one’s own company, copying these practices, as indicated by Dr. John Sullivan in this article, can only assure that you won’t gain a competitive edge via HR practices. Or, as stated in a Fast Company article “Best practices don’t make you the best. They make you the average.” In Sullivan’s article, he argues that distinguished HR organizations, such as Amazon and Google, share a common trait: they use “hypothesis testing” as a way to test assumptions either through statistical tests or experimentation to find out if a working premise or hypothesis is still true (or not true). An example of one hypothesis to test: The amount and length of onboarding impacts new hire turnover and performance. Using a data-driven approach to test our hypotheses can provide a source of competitive advantage, particularly since many organizations don’t use this approach. The article provides a few examples of different HR-based hypotheses as well as a 6 step framework for carrying out hypothesis testing.
