You're not trying to avoid all injuries, just the larger ones that would cause more significant damage or leave a permanent mark.

Since the global pandemic swept the globe a few years ago, one area that has yet to recover (and continues to decline) is the quality of shipped software. 

In the event of an OS-Level issue, are you able to continue working on another device or computer?

A test case should be repeatable. There is an age-old example of how this can be done badly.

Set yourself up for success by splitting out your setup, functionality, and teardown within your test automation.

I see a variant of this question asked weekly in the r/QualityAssurance subreddit and figured it would be helpful if I summarised the pros and cons I've read for each test framework.

Software development teams can be divided into two camps: those who build Dark Mode early and those who choose to wait.

Everyone builds their own single-use Test Framework just so the next employee or agency can park it and start over on their own single-use framework.

Most modern tooling uses services such as Sentry or NewRelic to boost observability in production and lower-down environments.  With the convergence of certain things, like browser render and JavaScript support, the requirement to test across different browsers and devices has become almost pointless and just an additional time and cost sink.

It does not matter what automation testing tool or framework you use. If you want to deliver a quality product to your users, you should also want to give a testable product to your testers. Adding Test IDs is the single best way to massively improve your testability, regardless of the automation test tool or framework.

Confidence. Without confidence, the activities have little to no value and all but real costs. But out of all of the buzzwords you can name, none directly mean code, or the requirement to write more code to achieve said Confidence.

100% of issues found by users happen on your production system. With the increasing rise of Shift-Left testing, we have seen the gap between testing and end-users widen into a chasm.