Past experiments have demonstrated that humans (with or without formal training) are quite good at visually identifying the structure of time series. Trend is a key component, and arguably the most relevant to practice, as many of the forecasts that affect our lives have to do with potential increases or decreases of economic variables. Forecasters and econometricians often rely on formal statistical tests, while practitioners typically use their intuition, to assess whether a time series exhibits trend. There is fairly limited research contrasting these two. Furthermore, sometimes trend is understood with a rather vague definition. I do not think it is an exaggeration to suggest that even experts often can be confused on the exact definition (and effect) of a unit root and a trend.
To understand more about this, I set up a simple experiment to collect evidence how humans perceive trends. The experiment below asks you to distinguish between trended and non-trended time series. Every 10 time series that you will assess it will provide you with some statistics on your accuracy and the accuracy of some statistical tests (by no means an exhaustive list!). It also provides overall statistics from all participants so far. As you can see, it is no so trivial to identify correctly the presence of trend! What do you think, can you better than the average performance so far?