[3rd3]

Of course each emergency is one too much, but I wonder what are the sample sizes? Are the counts statistically significantly different from each other? Another thing to consider: Emergencies are rare events and so a small difference in circumstances can make the outcomes vary substantially. Is a well oiled manufacturing pipeline like a Audi factory comparable to a new factory that has not rounded all sharp corners yet?

[xur17]

> Is a well oiled manufacturing pipeline like a Audi factory comparable to a new factory that has not rounded all sharp corners yet?

I wonder about this as well. It would make sense that a brand new factory that is rapidly growing would experience more safety issues. No idea if this is a reasonable increase though.

[oaiey]

Well ... or the owner of the company operates on Zucks quote of "Move fast and break things". When break does not mean software or money, than this is no longer a fun quote.

[Timon3]

I would expect fewer safety issues, since learnings from previous years can be taken into account during design, which isn't easy to do in existing factories.

[morsch]

Both effects could be in place at the same time, in which case you would expect a higher rate of workplace accidents initially, and a lower rate in the mid- to long-term.

[Timon3]

Until either of us finds studies on this topic it's meaningless speculation, because I honestly can't imagine more accidents, even initially, if things are done as they should be.

[gtowey]

"Move fast and break things" isn't supposed to apply to your workers' health.

[nerdponx]

3x as many emergencies per employee (not total, per employee) would be at least moderately alarming even if the numbers were 0.003 accidents per employee at Tesla and 0.001 accidents per employee at Audi.

I wouldn't get too hung up on statistical significance here. Whenever someone brings up statistical significance, I always like to ask: "significant with respect to what? by what standard?"

However it might be interesting to consider whether 3x is normal for all new production facilities. But that's a separate question.

[3rd3]

Even though p-values can be hacked, they are very useful when they aren't. At p = 0.1 I'd ignore the finding because there would be a 10% chance it was explained by random chance. p = 0.01 would pique my interest. p < 0.001 I'd accept it as true, but I'd still watch out for systematic biases such as comparing new to old factories.

[nerdponx]

Right, if you're at the point of constructing some kind of principled estimate of variation in the data then I think you have a pass to at least talk about "significance". But in that case I'm sure you're aware that this requires a particular hypothesis test in mind, not just an abstract notion of "significance", and that p-values interpreted as "strength of evidence" are problematic.