Three questions about the article "Ditch p-values. Use Bootstrap confidence intervals instead" I am not a statistician by training and I was asked by students to explain them an article called "Ditch p-values. Use Bootstrap confidence intervals instead" . The author seems a prominent academic, however, I am confused about some of the material there. Please, ignore this post if it seems too long for you. I cut it to just 3 questions, I will infer other answers based on these.

Let’s take a simplified but revealing example: we want to determine Robert’s citizenship. Null hypothesis: H0, Robert is a US citizen. Alternative hypothesis: H1, he is not. Our data: we know that Robert is a US senator. There are 100 senators out of 330 million US citizens, so under the null hypothesis, the probability of our data (i.e., the p-value) is 100 / 300,000,000 ≈ 0.000000303. Per the rules of statistical significance, we can safely conclude that our null hypothesis is rejected and Robert is not a US citizen.

Am I right that this is not a p-value (which is the probability to see this or more extreme value of a test statistic)? Is it a correct procedure for a statistical testing? I have a gut feeling that it is a wrong situation to apply hypothesis testing, but I can not formally answer why.

P-values were invented at a time when all calculations had to be done by hand, and so they rely on simplifying statistical assumption. Broadly speaking, they assume that the phenomenon you’re observing obeys some regular statistical distribution

It seems to be wrong, but the question is: can we say that non-parametric tests also rely on some regular statistical distributions? Not only they have assumptions, but also, technically, their statistics also follow some distributions.

Let’s say that a business decision-maker is pondering two possible actions, A and B. Based on observed data, the probability of zero or negative benefits is:
0.08 for action A
0.001 for action B
Should the decision-maker pick action B based on these numbers? What if I told you that the corresponding 90%
confidence intervals are:
[-0.5m; 99.5m] for action A [0.1m; 0.2m] for action B Action B may
have a lower probability of leading to a zero or negative outcome, but
its expected value for the business is much lower, unless the business
is incredibly risk-averse.

Can we, based on confidence intervals, say, what is an expected value? Is in this situation a clear decision? I always thought that confidence intervals are not necessarily symmetric, but I started to doubt here.

1. Citizenship example
This seems to be a poor but valid test. It makes sense with an extreme p-value cut off $\alpha = 0$. This way we would only reject those citizens whose profession is not found anywhere in the US. So maybe we would say "Robbert cannot be a US citizen because he is a suicide bomber and there are no suicide bombers in the united states" - a valid conclusion. The same in hypothesis testing terms: "$p = 0$ and  so the null hypothesis is rejected (obtaining observed sample from H0 is impossible)." Different p-value cut-offs are then just compromise thresholds about how strict we want to be.
2. p-values, calculations by hand, and assumptions
p-values are not tied to computations other than counting the number of occurrences when generating samples for the null hypothesis. And that generation can be performed by physical experiments in the real world. Moreover, we can use p-values for testing non-quantifiable or subjective things. So p-values can stand on their own as a philosophical construction without the need for concrete calculations, and there is no point in stating that they were invented in the age when calculations were performed by hand.
And p-values themselves don't assume anything. It's the statistical tests that have assumptions. And even they are very flexible about that. Sometimes, for example, the assumption is about the sampling distribution of the parameter, and not the observations themselves.
In addition, and I think author acknowledges this in the text, non-parametric tests also make one big assumption - they assume that the observed sample represents all the points from the population. The question is then simply which assumption is more warranted. A simplistic obvious case is with frequencies. Say you want to test if the chance of manufacturing defect is bigger than 5%, you get 10 samples and all of them are without defects. What would you be bootstrapping with 10 zeroes?
3. Confidence intervals
Confidence intervals are a flip-side of the p-value. With p-value you test how surprising your observed sample is given some theoretical null hypothesis and with a confidence interval you define a set (mostly an interval) of hypotheses that would not be surprising given the observed sample. It's somewhat strange to me that so many people criticise one for the other.
4. Other
One different point made against p-values in the text is about business people, to whom the values were reported, didn't understand them. But the answer is simple - don't show people things they might misunderstand. And I really doubt that someone not familiar with the intricacies of a p-value will be able to interpret confidence intervals without mistakes. "The job of a p-value is to prevent you from making a fool of yourself, and not report things as significant" - quoting someone (forgot who, sorry).
