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Comment by jordigh | Hacker News Reader

It's not a negative. You have to demonstrate that there are as many women as men, you have to demonstrate that their grants are comparable to men's, and you have to demonstrate that most women speak highly of the selection process and how fair they found it.

Please catalogue your evidence with citations whenever possible, and make sure you use reputable sources. Please also disclose your precise statistical procedure and experimental setup so that we may assess the validity of your findings. The questionnaires that you have given to women to assess their satisfaction are also of interest, as changes in wording can elicit different responses. I hope your experiment procedure has controlled for that.

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What are you on about?

Person A: "I observe a bias".

Person B (me): "Please show me the bias. I haven't seen it."

You (to me): "Prove that you don't observe a bias".

Christ, the level of discourse in these conversations is abysmal. We've reached the point where asking for citations is met with the response of "citation needed".

There's no need to paraphrase a conversation that is already present. The problem is you didn't only ask for citations, on something that was also clearly an anecdote, but also made your own greater claim i.e. the absence of something, without even providing your own anecdote. That is what you are being asked for citations on.

Sure, you can nitpick over the use of the word prevalent, but it's quite clear from the context that the conclusion was drawn on basis of the experience described (other than the article).

Then you've misunderstood: "claims of X seem exaggerated" can (and should) be taken as "the evidence for X is weak" (hence my request for more).

Anyway you want to cut it, this is a completely pedantic derailment of the original point: OP is making completely empty declarations.

>Person B (me): "Please show me the bias. I haven't seen it."

You didn't only disbelieve his statement, you offered a different claim.

A bias is not bigfoot. You can clearly prove that there is fairness ("no bias", but we can phrase your negative as a positive) by simply showing that things are equal. All you have to do is count males and females and show that they are equal.

If things are not equal, you can still prove that there is no bias by asking the disadvantaged group for their opinion. It's not like polling this group is impossible.

> by simply showing that things are equal.

Statistics do not work like this. You either positively reject the null hypothesis (i.e.: "these things are different") or you fail to reject the null hypothesis (i.e.: "I cannot assert these are different").

Formally speaking, I am doing the latter.

Informally speaking, everybody else understood my point as a rebuttal to OP's. More explicitly: "claims of X seem grossly exaggerated" should be taken as "I haven't seen compelling evidence for X" -- it may be awkwardly phrased, but I suspect you know exactly what I meant.

There are more statistics than a simple frequentist-style hypothesis testing. You can totally make a probability statement about equality if you allow Bayesian-style statistics. Frequentist-style hypothesis testing was designed for a time when computations were done by hand and data was scarce, but we can do better now.

You can also just outright swap around your null and alternative hypotheses for a frequentist-style test. For example, your null hypothesis could be "proportion of women to men is 0.1" and your alternative hypothesis is that it isn't, for a standard two-tailed test.

No demonstration is needed, because no claim is being made - The author explicitly states it is their experience that contradicts the original claim; While the original claim states as fact the claim being made, with no qualifier* suggesting that this is subjective opinion.

* Well, other than maybe the word 'belief', but this isn't clear imo

I'll accept this.

In which case I'll reiterate my request that he provide examples, since I've not observed the effect he seems to see all around him.

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