- The value, I mean I think the positive values of hypothesis, is that once you make that hypothesis, you should be able to think of experiments that test that hypothesis. So, it's an experimental generator. And that's very powerful. - And that hypothesis, sometimes people equate it to like some big, grand theory. It could be like a very, very small hypothesis, but I would say it's a tool for you to think about a system being able to ask an intelligent question and being able to design intelligent experiments. I would also say it should be a flexible tool, not an inflexible one. I think one of the general axioms, that I think everyone tells like a young scientist, although sometimes I think it's the senior scientists that sometimes need a kick in the pants here, as well, is you should never be wed to your hypothesis. That is a statement that's often made, and that is absolutely true. - I mean, in grad school I was always taught, do the experiment that will disprove your hypothesis. And you know, sometimes that experiment doesn't exist, and sometimes it's scary to do that experiment, 'cause you are wedded to your hypothesis, right? That is your baby. Like, you got there, you worked on it, you got it. But, it's not about being right, it's about how it works. - I think a common mistake that students that are studying in periodical research make when they are first looking at data is that they think that the hypothesis needs to be proven right. They think that the hypothesis is a, that the experiment works when the hypothesis is correct. So, I like to make an analogy with, this is one of the hardest parts. It was of the hardest parts for me when I started as faculty, because this was kind intuitive. It was something that I had learned, but I couldn't quite verbalize it, so. I will tell the students, for example, I will tell them, this is the analogy that I made, I will tell them, look, there's a door over there, go and open it. I hear a lion roaring, there must be a lion, open it, you're gonna discover the lion. And they will go there, they will open the door, they will look inside, they'll come back and they said, nope, no lion. And I still hear the roar, so I go there, I open the door, and I find a tiger, and I tell the student, you didn't see the tiger? Like, how could you not see the tiger? There's a huge tiger here, this is really exciting. And the student will say, yeah, but it's not a lion. So, essentially I will send the student in with a given hypothesis, and they will make a really exciting discovery, but they will come back and say the experiment didn't work. They will say the experiment didn't work because they're so used, from their classes, that when they get asked a question, there's a right answer and only one right answer, that if they don't get that answer, which they think is the hypothesis, then they feel that the experiment didn't work. I think that's a very common mistake with starting experimentalists. - You know, I think it's this concept of being self-critical or not about your hypothesis. We often come up with a hypothesis because, well, we've thought of an idea, so that's obviously a very exciting thing. And obviously, that someone will need to be encouraged. Because, you know, you want to think of new ideas and you want to think of ways in which biological systems work. I think that's very exciting. But, at the same time, that excitement for seeing if your idea is right, which is a very compelling, you know, emotional way to approach your project, and it is motivational. It has to be tempered with this critical eye that the hypothesis could well be wrong, and I think applying it correctly means yes, trying to see if you're right, that would be a great thing, but it also implies, like, finding out as quickly as possible whether it is wrong. And building that into your experimental thinking and because if every, your whole way of thinking about it is by designing experiments that are continuously trying to see if you get little bits of clues, whether your hypothesis of right is right, that may not be as compelling an experiment to really design a very good experiment that is definitive about whether your hypothesis is wrong. That may be the better experiment to do. - Have a healthy attitude that biology is really complex and you're very likely to get fooled, and so you should be constantly thinking about how could I be wrong. And if you're constantly thinking about how I could be wrong, then you're gonna actually try to design things to be rigorous to make sure that my interpretations are correct and how evaluated data is correct. So, you know, a healthy opt of skepticism about your own accomplishments is good, and then also taking advantage of the wisdom that's out there.