fela

I think the best term would have been statistically surprising, because it strongly hint at the fact that the result would be surprising under the null hypothesis, witch really is all that "statistically significant" really means. Sometimes surprising results happen, but all other things being equal they might hint at the null hypothesis being false. I could also live with "statistically interesting". "Detectable", suggested in another comment, seems to have some of the same issues as significant, it is too strong and seems to imply that now we know something is really there.

I don't think this is true unless you have a ridiculously high electricity bill. When I checked, one intercontinental retour flight was roughly equivalent to my yearly electricity bill, in terms of CO2 emissions. I have to admit I'm not sure how to reconcile this with CO2 credit prices, but I'm quite sure it's not really possible to offset the CO2 emissions of a intercontinental flight with 20 euro, that would mean that a minor tax on flights would make them practically carbon neutral, this is definitely not the case.

I assume you meant: If an airplane is as safe as average then it has PUT_NUMBER chance of having 2 incidents after 150k flights. 0.01% is actually the number I'm getting, assuming parent estimates are correct and making naive assumptions. In other words only 1 every 10 000 airplane models will have 2 incidents that early on if they are of average safety.

That is different then stating the probability of it being as safe as the average airplane, which you can't do as easily without additional modelling/priors and bayesian statistics.

I like to split the theorem in the following way:

P(Hypothesis|Data) = P(Hypothesis) * evidence_factor

P(Hypothesis) is the prior probability of the Hypothesis being true, in other words the probability we gave to the Hypothesis before seeing any of the data we are using in the theorem. When new data is observed, we use Bayes' theorem to update our believe in the hypothesis, which in practice means multiplying our prior probability by a number that depends on how well the new data fits our hypothesis. More precisely:

evidence_factor = P(Data|Hypothesis)/P(Data)

So it is the ratio of how likely our data is if our hypothesis is true, compared to (divided by) how likely it is in general. If it is more likely to occur in our Hypothesis, our probability of it being true increases, if it is more likely in general (and thus also more likely in case our hypothesis is not true, you can prove mathematically that those two statements are the same), then our believe in the hypothesis decreases.

TLDR: Prob(Hypothesis after I have seen new data) = Prob(Hypothesis before I saw the new data) * (how likely I am to see the data if my hypothesis is true, compared to in general)

Don't you need time to even define movement? Movement is a change of the position with respect to time. So without time you can't have movement. But obviously you are right that the two concepts are strictly related, that doesn't mean time doesn't "exist" (however you define "exist").