The scientific method is well understood by schoolchildren in theory, but thanks to the realities of schooling systems they are rarely if ever exposed to its actual practice. This is because the business of science can be quite expensive. Every experiment takes time and nontrivial amounts of capital, much of which may be irreversibly lost in each experiment. As such, academia is far behind modern development organizations. In most cases they are not even aware to the extent that we have made great strides towards actually doing experimentation.

Some of this is due to everyone capable of making a difference toward that problem being able to achieve more gainful employment in the private sector. Most of it is due to the other hard sciences not catching up to our way of experimentation either. This is why SpaceX has been able to succeed where NASA has failed -- by applying our way to a hard science. There's also a lack of understanding at a policy level as to why it is the scientifically inclined are overwhelmingly preferring computers to concrete sciences. The Chinese government has made waves of late claiming they wish to address this, but I see no signs as of yet that they are aware how this trend occurred in the first place.

Even if it were not the case that programming is a far quicker path to life-changing income for most than the other sciences, I suspect most would still prefer it. Why this income potential exists in the first place is actually the reason for such preference. It is far, far quicker and cheaper to iterate (and thus learn from) your experiments. Our tools for peer review are also far superior to the legacy systems that still dominate in the other sciences.

Our process also systematically embraces the building of experiments (control-groups, etc) to the point we've got entire automated orchestration systems. The Dev, Staging/Testing and Production environments model works quite well when applied to the other sciences. Your development environment is little more than a crude simulator that allows you to do controlled, ceteris-paribus experiments quickly. As changes percolate upward and mix they hit the much more mutis mutandis environment of staging/testing. When you get to production your likelihood of failure is much reduced versus the alternative. When failures do happen, we "eat the dog food" and do our best to fix the problems in our simulated environments.

Where applied in the other sciences, our approach has resurrected forward momentum. Firms which do not adopt them in the coming years will be outcompeted by those that do. Similarly, countries which do not re-orient their educational systems away from rote memorization and towards guided experimental rediscovery from first principles using tools very much like ours will also fall behind.