Psychology of People Analytics

Why is scientific knowledge poorly used in organizations?

I learned that an interview is generally a very poor selection instrument. You can improve this poor instrument a bit by conducting structured interviews, and you can improve the selection process a lot by adding tests and assessments. Still today, in most organizations candidates are selected based on the outcomes of a series of unstructured interviews.

The use of Bonus System

There is very little evidence that, for most jobs, bonus systems help to change the behavior of people in the right direction. Still many HR teams in many organizations spent a lot of time on the design of sophisticated bonus systems.

If scientific knowledge is poorly used, what will happen with all the analysis and recommendations of the newly established people analytics teams? Let’s see how some of the cognitive biases are important for people analytics, and how we can improve the impact of people analytics by taking them into account.

Some cognitive biases that are important for people analytics

  1. Action bias
  2. Algorithm aversion
  3. Assuming a normal distribution
  4. Authority bias
  5. Availability bias
  6. Confirmation bias
  7. Hindsight bias
  8. Information bias
  9. Fallacy of the single cause
  10. False causality
  11. Observer-expectancy effect
  12. Framing effect
  13. Generalizing one’s personal experience
  14. Insensitivity to sample size
  15. Overconfidence effect

Implications for people analytics: This bias can be seen a lot in the workplace. Read also the entry at ‘Algorithm aversion’. This bias can be a real hurdle for people analytics. Senior leaders are often appointed because they are confident, and some of them need a lot of convincing before they belief new facts that are not in line with their thinking. Presenting and selling facts and analysis is an integral part of the job of people working in people analytics.

There are many more interesting cognitive biases, that are important for HR and people analytics. Ignoring psychology is no option. Assuming that ‘the facts will speak for themselves’ can be considered naïve. There is a benefit for people analytics professionals to learn more about psychology and important cognitive biases because no one is exempt from these biases. Awareness is the first step, using and neutralizing some of the biases is an important condition for the success of people analytics.