Video: Observer Effects and Data

The easiest way to recognize observer effect problems in data science come to us from observing human behavior. Social scientists have known for quite some time that people behave differently when they know they are being observed. Such changes in behavior have different names, depending on whether the change is intentional or unintentional and also on the motivation of the observed.

When using data science to understand behavior, consider the impact on the behavior if the observed population is aware of the measurement. Consider also the ways to understand those changes in behavior over time.


As we use new methods to understand complex sets of data, we must be aware of how our own behavior as researchers might be changing our conclusions. We must challenge how our thinking itself is changing based on what we are learning.

We are only at the beginning of understanding how the environments we live in will change in the era of big data. Surrounded by things that create data and things that analyze that data, data about our data, and observations intended to change our behavior, we continue to learn what it all means. If we are aware of observer effects, we can have a newer, richer understanding that was never before possible. That understanding can help us to be better detectives, better advisors, better scientists, and better curators of the amazing data assets at our disposal.