1. Confidence**.
Instilling confidence is probably the most important thing a mentor can do. Science is about unknowns and learning how to become an expert. And that requires confidence.
So how do you instill confidence?
2. Basic programming and learning how to “script”.
This was a real catalyst for me and a huge boost to my confidence. Once I had mastered some basic programming in R, it allowed me to start treating data like an experimental subject. Want to understand what happens when you ignore pseudoreplication in your data? What about how collinearity might influence the results of your analysis? It’s not too hard to write a simulation to figure that out. A lot of basic programming is troubleshooting, a useful and transferable skill. Acting like an experimenter also comes naturally – I see it all the time with my 4-month-old daughter!
Learning how to write scripts is also key to making your workflow efficient and reproducible. Filtering, tidying, and graphing your data is 90% of the work. Doing that through code is way more efficient and leaves a record of what you did, making it easier to correct errors later on. And if you can generate publication-quality graphs purely through code, it will save you a huge amount of time making tweaks. And believe me, you will need have to make a lot of tweaks. Finally, scripting means your work can be used by others (including, and perhaps especially, your future self).
3. Students are scientists, too.
There is nothing I’ve done that couldn’t be done by an undergraduate, if they had enough time. One of the best things grad school was our weekly seminar series. We’d have an MSc exit seminar one week followed by a distinguished visiting professor the next. As a student, your work is every bit as important.
4. Treating feedback as an opportunity.
I think it’s important to provide students with lots of constructive feedback – and also, to help them develop an ability to deal with it. In science (and in life), rejection happens. I got another huge boost when I stopped worrying about negative feedback and started looking at it as a problem-solving opportunity. This is a broadly transferable skill.
Taken together, the points above are pretty circular: it takes confidence to handle feedback, but also dealing with feedback forces you to gain confidence. So “fake it until you make it” really works. As a mentor, I think it’s important to treat students as fellow scientists, to provide them with lots of opportunities to act as peer reviewers and reviewees, and to model the process of using feedback to solve problems.
Update to #1 above, on confidence: I also try to emphasize that the value of science is based on the quality of the data collected and clear dissemination of the results – and not whether it supports a particular hypothesis, or has a p-value < 0.05. I think this is a major stumbling block for a lot of students. Your thesis does not hang on the results of one test! The cure to this kind of thinking includes a better understanding of what p-values really mean and the limitations of null hypothesis statistical testing (NHST), and a focus on reporting the data (including effect sizes, confidence intervals, and individual variation).
** Related: I think a lack of confidence is a major cause of the leaky pipeline for women in STEM (and perhaps other under-represented groups). Many women choose careers outside of science despite aptitude (see for example this 2009 study by Ceci et al.). There’s some very recent evidence that gender stereotypes about aptitude – which could shape children’s interests as well as their confidence – begin as early as 6 years old (see here).