3 ms·

> ...I don't think it's always true... I will have to re-evaluate my thinking on this. My knee-jerk response is to say this is wrong. I have seen too many ca

> ...I don't think it's always true... I will have to re-evaluate my thinking on this. My knee-jerk response is to say this is wrong. I have seen too many cases where people report initial results on some elaborate model as if they explain more aspects of the data, but then the results fail to generalize, and the theory, model or implementation turns out to be flawed or fragile. It gets a publication, but the state of the art does not advance. But I notice that some younger investigators I have worked with tend to be more careful, with things like releasing code/data openly, being serious about code/model reuse, doing more careful verification/validation, being more rigorous about test/training data. It seems to be a spectrum of better research practices rather than one thing, like “better software engineering“. Such a more principled approach should prove itself over time, because Nature cannot be fooled.

btrettel 8y ago
Yes, "being careful" refers to a variety of different techniques, not just better software engineering. What has ended up being publishable in my case wasn't related to better software engineering, rather, being more careful about model derivations and more rigorous about the available data. But it could have been software improvements. To be clear, I don't think everyone needs to be as careful as I am. A few researchers in a particular subfield spotting errors can have large benefits for everyone. And those few may catch the vast majority of the errors. There are diminishing returns to adding more people focused on being rigorous.
- petschge 8y ago
  This is a very important point. Your budget of "being careful" might be best spent on better software engineering. But it is also possible that you should rather spend it on other things such as better mathematical model, better input data sets or something else. And a (sub)field prospers most if different groups and researchers spent there (very limited and expensive) time on different things. Trying to shame everybody into following the magic 27 rules of software engineering is a step back, not forward.

btrettel 8y ago

Yes, "being careful" refers to a variety of different techniques, not just better software engineering. What has ended up being publishable in my case wasn't related to better software engineering, rather, being more careful about model derivations and more rigorous about the available data. But it could have been software improvements. To be clear, I don't think everyone needs to be as careful as I am. A few researchers in a particular subfield spotting errors can have large benefits for everyone. And those few may catch the vast majority of the errors. There are diminishing returns to adding more people focused on being rigorous.

petschge 8y ago
This is a very important point. Your budget of "being careful" might be best spent on better software engineering. But it is also possible that you should rather spend it on other things such as better mathematical model, better input data sets or something else. And a (sub)field prospers most if different groups and researchers spent there (very limited and expensive) time on different things. Trying to shame everybody into following the magic 27 rules of software engineering is a step back, not forward.