“Flow”, success and hapiness

Yesterday after our Biophilosophy Society session, I had an interesting chat with Matan about whether switching research subjects is good or bad.

Today, just by chance I ended watching this video from 2008. I think it nicely answers what we discussed. NOW WATCH THE VIDEO… (19 minutes long, but really worth your time)

Only after watching the video, you will understand what follows:

If you you feel that the field your are working on, does not provide enough of a challenge to get you into ‘flow’ at least now and then, then you have two options: find new challenges that you find exciting within your current discipline, or shift your interests to another discipline. Which of these routes you take, is quite irrelevant as long as you can reuse enough of your current skills in the new subject to be within your safe zone of comfort. Reusing the skills does not require the skills to be used in the same way as in the previous discipline, just that you find a way of making use of your skills even if by analogy when analysing a new problem.

I am currently participating in the “Leadership training” organized by the university, and in one recent meetings of my research group when discussing how to better work as a group, I emphasized that for every member of the group their work should be fun. This idea is formalized, and backed with data in the talk in the video I embedded above. So, if you skipped it, scroll up the page and watch it!


Some frequent ways of unwillingly misrepresenting experimental results

Many students and some researchers are ignorant of the fact that any of the following practices are statistically invalid and could be considered to be ‘research-results manipulation’ (=cheating):

  1. Repeating an experiment until the p-value becomes significant.
  2. Reporting only a ‘typical’ (=nice-looking) replication of the experiment, and presenting statistics (tests of significance and/or parameter estimates such as means and standard errors) based only on this subset of the data.
  3. Presenting a subset of the data chosen using a subjective criterion.
  4. Not reporting that outliers have been removed from the data presented or used in analyses.