Difference between revisions of "Talk:2494: Flawed Data"

Explain xkcd: It's 'cause you're dumb.
Jump to: navigation, search
(Thoughts on learning from mistakes.)
(Biogen Reference?)
Line 10: Line 10:
  
 
On first reading, I was thinking the Good approach would be to go out and run new experiments and measurements, incorporating the lessons from their flawed data, to avoid making the same mistakes again.  This can be quite expensive, but it is really the only way to increase the validity of the data.  Just saying "We can't trust our conclusions," throws away the opportunity to learn from earlier mistakes and come up with better measurements next time.  [[User:Nutster|Nutster]] ([[User talk:Nutster|talk]]) 14:38, 28 July 2021 (UTC)
 
On first reading, I was thinking the Good approach would be to go out and run new experiments and measurements, incorporating the lessons from their flawed data, to avoid making the same mistakes again.  This can be quite expensive, but it is really the only way to increase the validity of the data.  Just saying "We can't trust our conclusions," throws away the opportunity to learn from earlier mistakes and come up with better measurements next time.  [[User:Nutster|Nutster]] ([[User talk:Nutster|talk]]) 14:38, 28 July 2021 (UTC)
 +
 +
 +
Is this a reference to Biogen? Doing some motivated post hoc subgroup analysis to get Aduhelm approved.

Revision as of 16:26, 28 July 2021

For the first time in a very long time I was the first to make an attempt at the main explanation. I guess this comic came out very late then? Or just late up on explain xkcd? Seems like the Monday comic first came up on Tuesday in many countries including those in Europe. But guess it was still Monday in the US, at least in the western parts? I hope this is not as bad an attempt as Cueball's research strategies in the last panel :-) --Kynde (talk) 07:05, 27 July 2021 (UTC)

Isn't it related to a recently published article[1][2] about bias introduced into AI by humanly-biased data?
Reports of bias in AI have been in the news for several years. Most notably, facial-recognition systems that are bad at distinguishing faces of black and brown people. Barmar (talk) 13:58, 27 July 2021 (UTC)
"On two occasions I have been asked [by members of Parliament], 'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?' I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question." - Charles Babbage ; note that century and half passed since that quote and people STILL somehow expect computer will be able to reach correct results based on wrong data. -- Hkmaly (talk) 17:23, 27 July 2021 (UTC)

This explanation has some good ideas for what these things mean, but it goes into them in excessive detail, which doesn't leave a lot of room for other ideas to be included side-by-side. I think that might be common. I was just thinking that there are a lot of ways extra math is used to produce worse conclusions: as soon as you have to work more to find what is good, occam's razor says you are less likely to be relevant. Similarly there are a lot of ways that AI is used to work with data, but its power greatly surpasses its ability to reflect the underlying meaning of things. For example, the existing data can be extended without being scrapped, and look completely real in every known respect, but that doesn't mean that any new information is included in what is generated, since the only data to work with is what was already there. 108.162.219.98 16:19, 27 July 2021 (UTC)

There are various techniques in Machine Learning to augment the training data, which can include generating fake data that looks like the real data; one such technique is using Generative adversarial network (GAN).

On first reading, I was thinking the Good approach would be to go out and run new experiments and measurements, incorporating the lessons from their flawed data, to avoid making the same mistakes again. This can be quite expensive, but it is really the only way to increase the validity of the data. Just saying "We can't trust our conclusions," throws away the opportunity to learn from earlier mistakes and come up with better measurements next time. Nutster (talk) 14:38, 28 July 2021 (UTC)


Is this a reference to Biogen? Doing some motivated post hoc subgroup analysis to get Aduhelm approved.