I find it very malicious to just drop numbers like this. It gives the impression that you state a simple fact, while the reality is much more complicated than that.
The studies on the subject require much more subtlety and statistical understanding when interpreting the results that a 2 line quotation to make a point.
There are a number of factors to take into account when giving credit to such studies:
- Is there enough observations to have statistically significant results? (i.e. do we have enough occurences of police strikes to really have meaningful results? can we have overwelmingly influencing factors not present in the studied samples: I guess it would likely highly depend on the city where the strike happens also)
- Did the sample properly allowed the isolation of the variable being studied against other influencing factors?
- What is the collinearity between the variables used for the regression? (i.e. if violence complaints are made on the spot, then less police means harder to fill complaints, it doesn't necessarily mean less violence)
"Freakonomics: A Rogue Economist Explores the Hidden Side of Everything" has a good introduction to the challenges of such studies, and discusses a bit the particular case of police.
Please don't introduce personal swipes like "malicious" into what's otherwise a fine HN comment. It usually provokes the person you're talking to into replying with worse; or if that doesn't happen, it causes other people to take your comment the wrong way (see https://news.ycombinator.com/item?id=20960084). It also breaks the site guideline against calling names in arguments:
But it can be equally malicious to just drop generic potential shortcomings of scientific studies, without any attempt to figure out whether they apply to the specific study under discussion.
I don't agree with this comment at all. Skepticism isn't denying the claim or advocating an alternative one, it is promoting thoroughness and responsibility. The uninspected claim is quite a bit more dangerous.
This particular flavor of skepticism does not promote thoroughness and responsibility, in fact it promotes laziness: "Why read (or even skim) through the article when I can just state these five ways in which the article might be bad and therefore its argument void?"
If you think broad skepticism or criticism of a scientific study based on class characteristics is admissible (some statistical investigations are poorly conducted, so this one might be poor too), why not trust based on class characteristics (scientific articles in Nature tend to be really good, so this one might be good too)?
You're right, I don't think either of these options is better than the other.
>I find it very malicious to just drop numbers like this. It gives the impression that you state a simple fact, while the reality is much more complicated than that. The studies on the subject require much more subtlety and statistical understanding when interpreting the results that a 2 line quotation to make a point.
And yet such numbers are dropped all the time. I also do not see callouts for number drops being consistently applied. For example in political subreddits sees number drops without callouts when it supports the lean of the subreddit and number drops with callouts when it does not.
Criticism of science seems to be unequally applied, and given how important equal application of criticism is to science being reliable, it creates a reliability problem.
As an outsider reading this thread I couldn't help but laugh at this.
Galanwe said phry was "very malicious", which is actually and not just tenuously a personal attack. Do you plan to delete their comment as well? Interesting standards on HN.
Please don't interpret as "standards" what is typically a simple case of us seeing one comment and not the other. We don't come close to seeing everything that gets posted here. If you run across something that didn't get moderated when it should have, the likeliest explanation is that we just didn't see it.
Edit: I've tracked down the comment you're referring to and indeed, no moderator saw it. I've replied to it here: https://news.ycombinator.com/item?id=20964408. In the future, if there's a comment you're concerned about, you should either flag it or email us at hn@ycombinator.com.
If you haven't checked specifically with us about a post, please don't draw conclusions about HN moderation—those are almost always non sequiturs. People usually jump to the idea that we secretly support the one side (where they didn't see us moderate) over the other side (where they did). That is reading patterns into randomness.
Edit: Also, could you please stop creating accounts for every few comments you post? We ban accounts that do that. This is in the site guidelines: https://news.ycombinator.com/newsguidelines.html. HN is a community. Users needn't use their real name, but do need some identity for others to relate to. Otherwise we may as well have no usernames and no community, and that would be a different kind of forum. https://hn.algolia.com/?sort=byDate&dateRange=all&type=comme...
"It lets everyone know it's safe to ignore you" is a personal attack. If you'd please review https://news.ycombinator.com/newsguidelines.html and stick to the rules when posting here, we'd really appreciate it.
Ok, I believe you that that was your intent. But "you" is a personal pronoun. If you use that pronoun and sling pejoratives, people are naturally going to interpret it as a personal attack, as I did. If you don't want to be read that way, the burden is on you to disambiguate that.
> Ad hominem (Latin for "to the person"),[1] short for argumentum ad hominem, typically refers to a fallacious argumentative strategy whereby genuine discussion of the topic at hand is avoided by instead attacking the character, motive, or other attribute of the person making the argument, or persons associated with the argument, rather than attacking the substance of the argument itself.
You failed at the "genuine discussion" part by citing pop econ horseshit.
There are a number of factors to take into account when giving credit to such studies:
- Is there enough observations to have statistically significant results? (i.e. do we have enough occurences of police strikes to really have meaningful results? can we have overwelmingly influencing factors not present in the studied samples: I guess it would likely highly depend on the city where the strike happens also)
- Did the sample properly allowed the isolation of the variable being studied against other influencing factors?
- What is the collinearity between the variables used for the regression? (i.e. if violence complaints are made on the spot, then less police means harder to fill complaints, it doesn't necessarily mean less violence)
"Freakonomics: A Rogue Economist Explores the Hidden Side of Everything" has a good introduction to the challenges of such studies, and discusses a bit the particular case of police.