9 ms·

What Data Can’t Do

wodenokoto 6y ago
The article seems to stop pretty early, as if something is missing. It’s an anecdote about a government incentive to have doctors see patients within 48 hours causing doctors to refuse scheduling patients later than 48 hours in order to get the incentive bonus. This is not an example of limits of data, but an example of perverse incentives.
- deleted 6y ago
  [deleted]
- justinclift 6y ago
  > It’s an anecdote about a government incentive to have doctors see patients within 48 hours causing doctors to refuse scheduling patients later than 48 hours in order to get the incentive bonus. That part is probably just the first 1/8th or so of the article (rough guess). Sounds like it was cut short for you?
- jabroni_salad 6y ago
  When a measure becomes a target, it ceases to be a good measure. This case could be said to be creating misleading data. If the doctor's offices aren't recording appointments more than 48 hours in advance, the System is losing visibility on the total number of people who want appointments. Every office will appear to be 100% efficient even though there is effectively still an invisible waiting list.
- polytely 6y ago
  Did you use reader mode? because I noticed that when I use firefox's reader mode it will cut off part of the article on the New Yorker site.
  - strathmeyer 6y ago
    We clicked on the link and were presented with two paragraphs. New Yorker articles usually don't show up correctly, I don't know why they allowed to be posted here. Acting like we don't know how to read a webpage is gaslighting.
    - polytely 6y ago
      Well that wasn't my intention at all, just a problem i encountered a few hours before I read your comment so it was fresh on my mind.
  - Fishkins 6y ago
    When I first opened the article in Firefox, I only saw the first two paragraphs (same as the OP). This was true whether it was in reader mode or not. I opened it in Chrome and saw the whole article. I just tried opening it in Firefox now (a couple hours later) and I see the whole article. If I switch to reader mode I do see it's truncated about halfway through, but I think that's a separate issue from what the OP was seeing.
  - wodenokoto 6y ago
    On iOS. Initially used reader mode, but switched because it seemed cut off. But also without reader mode I can’t see more than the NHS anecdote.
carlosf 6y ago
I am increasingly worried with people applying ML in everything without any rigour. Statical inference generally only works well in very specific conditions: 1 - You know the distribution of the phenomenon under study (or make an explicit assumption and assume the risk of being wrong) 2 - Using (1), you calculate how much data you need so you get an estimation error below x% Even though most ML models are essentially statistics and have all the same limitations (issues with convergence, fat tailed distributions, etc...) it seems the industry standard is to pretend none of that exists and hope for the best. IMO the best moneymaking opportunities in the decade will involve exploiting unsecured IOT devices and naive ML models, we will have plenty of those.
- CabSauce 6y ago
  Wait until you find out low many studies have been published in medical journals with serious statistical flaws.
- iagovar 6y ago
  ML looks (for many peole) like a way to circunvent your grumpy statiscian saying that the underlying data is worthless and/or you should focus on getting the data pipeline done properly for a logit model on your churn rate.
  - analog31 6y ago
    "Scientist free science," -- being able to optimize systems without understanding them, has been a dream of the business world since the dawn of time. There's always been a market for cookbook recipes that automate the collection of data, and interpretation of results. Before ML, there were "design of experiments," and "statistical quality control."
    - carlmr 6y ago
      >Before ML, there were "design of experiments," and "statistical quality control." Statistical quality control, at least the way I know it, is very useful in finding problems in your process. I'm also not sure how this fits with your premise. It's about optimizing systems by first finding out where to look, and then looking there in detail with expert knowledge, i.e. deep understanding of your system.
canadianwriter 6y ago
Data always needs to be paired with empathy. ML/AI simply doesn't have empathy so it will always be missing a piece of the overall pie. Let AI crunch the numbers, but combine it with a human who can understand the "why" of things and you can really kick butt.
- gnulinux 6y ago
  I agree with you, although, unfortunately, most -- if not all -- engineers I know would respond to this by complaining about how "a human who can understand the 'why'" cannot be automated.
sradman 6y ago
From the ungated archive [1]: > Whenever you try to force the real world to do something that can be counted, unintended consequences abound. That’s the subject of two new books about data and statistics: “Counting: How We Use Numbers to Decide What Matters”, by Deborah Stone, which warns of the risks of relying too heavily on numbers, and “The Data Detective”, by Tim Harford, which shows ways of avoiding the pitfalls of a world driven by data. Data is a powerful feedback mechanism that can enable system gamification; it can also expose it. The evil is extracting unearned value from a system through gamification not the tools employed to do so. I’m looking forward to reading both books. [1] https://archive.is/ynOm2 https://archive.is/ynOm2
kkoncevicius 6y ago
This article is not so much about the data, as it is about rules and thresholds used to divide that data into groups.
mdip 6y ago
Kind of love the initial story in the article about 48-hour wait times. I had a stint writing conferencing software for quite some time, and every once in a while we'd come across a customer requirement that had capabilities which were obvious to us developers "would be misused". As a result, we did the "Thinking, Fast and Slow" pre-mortem to help surface other ways that the system could be attacked (along with what we would do to prevent it and how it impacted the original feature). If you create something, and open it to the public, and there's any way for someone to misuse it for financial incentive (especially if they can do so without consequence), it will be misused. In fact, depending on the incentive, you may find that the misuse becomes the only way that the service is used.
- kazinator 6y ago
  When the doctor's office is inundated with patient visits, you cannot fix scheduling back-logs by fiddling with the scheduling algorithm, no matter how much data you have. Say the calendar is initially empty and 1000 people want to see the doc, right now. You can fill them all into the calendar, or you can play games that solve nothing, like only filling tomorrow's schedule with 10 people, asking 990 of them to call back. That doesn't change the fact that it takes 100 days to see 1000 patients. All it does is cause unfair delays; the original 1000 can be pre-empted by newcomers who get earlier appointments since their place in line is not being maintained.
- lifeisstillgood 6y ago
  conferencing as in 'ComicCon' or 'Zoom'? Can you give an example?
  - mdip 6y ago
    Haha, hadn't even thought of that -- Conferencing as in developing bespoke (and some white-label) software for organizations deploying Office Communications Server (and R2), Lync and ultimately Skype for Business (I do a little Teams work these days but I am focused on other areas, presently).
- strathmeyer 6y ago
lmm 6y ago
Does the use of statistics actually amplify misunderstanding, or merely reveal misunderstandings that were already there? In any of these examples given - predicting rearrests, infant mortality, or so on - it's hard to imagine that someone not using numbers would have reached a conclusion that was any closer to the truth. Data has its limits, but the solution is usually - maybe even always - more data, not less.
- mjburgess 6y ago
  It's pretty trivial to predict things without "data". Data just means using some measurement system to obtain measurements of some target phenomenon. Many targets cannot be measured, or have not occurred to be measured. Reasoning counter-factually is trivial: What would happen if I dropped this object in this place in which an object, of this kind, has never been dropped before? Well apply relevant models, etc. and "the object falls, rolls, pivots, etc.". This is reasoning-forward from models, rather than backwards from data. And it's the heart of anything that makes any sense. Data is not a model and provides no model. The "statistics of mere measurement" is a dangerously utopian misunderstanding of what data is. The world does not tell you, via measurement, what it is like.
  - lmm 6y ago
    But where does that model come from if not from data? We might use some logical principles to inform our model - but don't those principles themselves also ultimately have to be inferred from data about the world?
    - mjburgess 6y ago
      measurement of our bodies engaged in deliberate action measurements of the world resolve ambiguities; they do not 'contain' descriptions of the world, not can they provide any measurements of objects must be interpreted by models
visarga 6y ago
Data is very limited, indeed. We can't predict outside the distribution, or unrelated events (without a causal link), or random events in the future. We should be humble about the limits of data.
- _rpd 6y ago
  Sure, but coding a human-equivalent response to such events is trivial (because no one and nothing responds well to such events).
Ozzie_osman 6y ago
Data is not a substitute for good judgment, for empathy, for proper incentives. The article focuses on governments and bureaucracies but there's no better example than "data-driven" tech companies, as we A/B test our key engagement metrics all the way to soulless products (with, of course, a little machine learning thrown in to juice the metrics). I wrote about this before: https://somehowmanage.com/2020/08/23/data-is-not-a-substitute-for-good-judgment/ https://somehowmanage.com/2020/08/23/data-is-not-a-substitut...
- mminer237 6y ago
  I think it's almost worse in tech because it largely works. If the government sets a flawed metric, their real goal of pleasing their constituents has failed and theoretically they either have to fix it or lose political support. But in tech, if your goal is just to make money, soullessly following data will often get you there, to the detriment of everyone else. Clickbait headlines will get you more views. Full-page popup ads will get you more ad clicks/newsletter subscriptions. Microtransactions will get you more sales. Gambling mechanics will get you more microtransactions. You can say it's a flawed metric, but I think in the end, most people just actually care more about making money than they do about building a good product.
- gen220 6y ago
  I've written the same sentence before! This is so cool! pardon the wall of text. Here's my thesis, curious to hear your thoughts. At some time around 2005, when efficient persistence and computation became cheap enough that any old f500-corp could afford to endlessly collect data forever, something happened. Before 2005, if a company needed to make a big corporate decision, there was some data involved in making the decision, but it was obviously riddled with imperfections and aggregations biases. Before 2005, executives needed to be seasoned by experience, to develop this thing we call "Good Judgement", that allows them to make productive inferences from a paucity of data. The Corporate Hierarchy was a contest for who could make the best inferences. Post-2005, data collection is ubiquitous. Individuals and companies realized that you don't need to pay people with experience any more, you can simply collect better data, and outsource decision-making to interpretations of this data. The corporate hierarchy now is all about how can gather the "best" data, where "best" means grow the money pile by X% this quarter. "Good Judgement" used to be expected from the CEO, down to at least 1-3 levels of middle management above the front-line people. Now, it appears (to me) to be mostly a feature of the C-Suite and Boards, and it's disappeared elsewhere. Long-term, high-performing companies seem to have a more diffused sense of good judgement. But these are rare. maybe they always have been? Anyways, as we agree, this has a tendency to lead in problematic directions. Here's my thesis on "why". Fundamentally, any "data" is reductive of human experience. It's like a photograph that captures a picture by excluding the rest of the world. Few people seem to understand this analogy, because they think photographs are the ultimate record of an event. Lawyers understand this analogy. With the right framing, angle, lighting (and of course, with photoshop), you can make a photograph tell any story you want. It's the same issue with data, arguably worse since we don't have a set of standard statistics. We have no GAAP-equivalent for data science (yet?). Our predecessors understood that data was unreliable, and compensated for this fact by selecting for "Good Judgement". The modern mega-corps demonstrate that we don't have a good understanding of this today, evidenced by religious "data-driven" doctrine, as you describe. People will say "hey! at least some data is better than no data!", to which I'll say data is useless and even harmful in lieu of capable interpreters. In 2021, have an abundance of data, but a paucity of people who are capable of critical interpretation thereof. I don't know if it's a worse situation than we had 20 years ago. But it's definitely a different situation, that requires a new approach. I think people are taking notice of it, so I'm hopeful.
kazinator 6y ago
> doctors would be given a financial incentive to see patients within forty-eight hours. Not measuring that from the first contact that the patient made is simply dishonest. "Call back in three days to make the appointment, so I can claim you were seen within 48 hours, and therefore collect a bonus" amounts to fraud because the transaction for obtaining that appointment has already been initiated. I mean, they could as well just give the person the appointment in a secret, private appointment registry, and then copy the appointments from that registry into the public one in such a way that it appears most of the appointments are being made within the 48 hour window. Nothing changes, other than that bonuses are being fraudulently collected, but at least the doctor's office isn't being a dick to the patients.
- Eridrus 6y ago
  It's really hard to design a not game-able metric. The problem here seems to be that doctors are under-provisioned for some reason, and so long wait times are a form of load shedding for the system. Without addressing this core issue, which individual clinics have little control over because they are generally boxed in by regulations over who can administer medical care, except to rush appointments (which they're probably already doing), there's not much they can do to solve the problem, so all they can do is try to game the rules or not get the bonuses.
  - deleted 6y ago
    [deleted]
  - closeparen 6y ago
    Doctors don’t get to bill for idle time. Being less than fully utilized is leaving money on the table. The idea here is presumably to compensate them for leaving gaps in their schedules.
    - Eridrus 6y ago
      What you're describing is effectively what doctors did: they left their entire calendar free until the last moment and only took appointments then. It turns out this is not actually what people want; people want this availability to exist, but also do not want to be turned away if they book ahead of time, which points to this being a capacity problem, not a scheduling problem.
williesleg 6y ago
solve covid, not a vaccine but a treatment, those are forbidden.
ppod 6y ago
This author has published a couple of articles like this at the New Yorker They all have this in common: the author works through some interesting and in some ways unusual cases where data or statistics have been improperly or naively applied, with some social costs. I really enjoy the articles themselves. Then the New Yorker packages it up with a cartoon and a headline and subheadline like "Big Data: When will it eat our children?" or "Numbers: Do they even have souls?", and serves it up to their technophobic audience in a palatable way. https://www.newyorker.com/contributors/hannah-fry https://www.newyorker.com/contributors/hannah-fry
- dsaavy 6y ago
  > *Numbers don’t lie, except when they do. Harford is right to say that statistics can be used to illuminate the world with clarity and precision. They can help remedy our human fallibilities. What’s easy to forget is that statistics can amplify these fallibilities, too. As Stone reminds us, “To count well, we need humility to know what can’t or shouldn’t be counted.”* I do have a problem with her conclusion here. Are numbers really lying if it's actually an incorrect data collection method or conflicting definitions of criteria for generation of certain numbers (like the example used in the second to last paragraph)? She seems to be pointing out a more important fact, which is that people don't question underlying data, how it was collected, and the choices those data collectors made when making a data set. People tend to take data and conclusions drawn from it as objective realities, when in reality data is way more subjective.
  - lupire 6y ago
    > Are numbers really lying if it's actually an incorrect data collection method or conflicting definitions of criteria for generation of certain numbers Obviously it's a figurative metaphor, but it's pretty clearly a case of "this supposedly objective factual calculation is presenting an untruth."
    - Nasrudith 6y ago
      You can still be very misleading with objectively true calculations. "There is very low stress on the patient's arteries and only a very small tear." - said patient bled to death and has only atmospheric stress now that their veins are bloodless. Less than 0.1% of their total vein area has a rip in it.
tppiotrowski 6y ago
"once a useful number becomes a measure of success, it ceases to be a useful number" Two other unintended consequences of incentives I learned in economics: 1. Increasing fuel efficiency does not reduce gas consumption. People just use their car more often. 2. Asking people to pay-per-bag for garbage pickup resulted in people dumping trash on the outskirts of town. Edit: Did more research after downvote. Definitely double check things you learn in college 1. The jury is still out: https://en.wikipedia.org/wiki/Jevons_paradox https://en.wikipedia.org/wiki/Jevons_paradox 2. Seems false https://en.wikipedia.org/wiki/Pay_as_you_throw#Diversion_effects_and_risks https://en.wikipedia.org/wiki/Pay_as_you_throw#Diversion_eff...
tomrod 6y ago
An absolutely fantastic article that captures my concerns as a user, purveyor, and automater of systems that help with numbers. I'm always very cautious regarding the jump from numbers informing to numbers deciding.
temp8964 6y ago
This article is totally gibberish. It's a terrible mixture of many unrelated things. Just because those things all have something to do with data (anything can be presented in numeric form), it does not make their issues are about data. First, the Tony Blair example is not about data. It is a failure of government planning. It's wrong politics and wrong economy. The G.D.P. example is laughable. G.D.P. is never intended to be used to compare individual cases. What kind of nonsense is this? And the IQ example. The results are backed by decades of extensive studies. The author thinks picking a few critics can invalidate the whole field. And look! The white supremacist who gave Asians the highest IQ, what a disgrace to his own ideology. Many more. I feel it's kind of tactic to produce this kind of article. Just glue a bunch of stuff, throw together with somethings seem to be related, bam, you got an article.
- cloogshicer 6y ago
  Gotta disagree here. This article is acknowledging a pattern, that data is misused in many different areas. I think the problem goes even deeper, which is a misunderstanding of the scientific method. Good discussion about this topic here: https://news.ycombinator.com/item?id=26122712 https://news.ycombinator.com/item?id=26122712
  - temp8964 6y ago
    "data is misused in many different areas" is not a valuable / informative point. There are many wrongs seem to have something to do with data, but in fact they are not. Like socialist economy planning will eventually fail, but then you would say they misused data. It seems relevant, but misusing the data is not the real cause of their failure at all.
    - not2b 6y ago
      Replace "socialist" with "large company". The companies gather data, establish metrics, and manage to those numbers, and often bad things result. Ever been in a company where some internal support function goes to hell because its top manager's bonus depends on a metric, and they can improve that metric by refusing to support the users (find excuses to close IT support calls without fixing the issue, etc).
jplr8922 6y ago
Confusing performance metrics and strategical objective is not a data problem, it is a human problem. It happens to a lot of people outside the usual Blair-WhiteNationalist-IQ crowd. I do not think that advanced technical knowledge in ML or stats is required to avoid this mistake ; it is the ability to perform valid counterfactuals statements. A good example of what I mean can be found on wikipedia : His instinctive preference for offensive movement was typified by an answer Patton gave to war correspondents in a 1944 press conference. In response to a question on whether the Third Army's rapid offensive across France should be slowed to reduce the number of U.S. casualties, Patton replied, "Whenever you slow anything down, you waste human lives."[103] https://en.wikipedia.org/wiki/George_S._Patton https://en.wikipedia.org/wiki/George_S._Patton Here, US general Patton is not confounding a performance metric (number of casualities) with strategic goal (winning the war). His counterfactual statement could be that ''if we slow things down, you are simply delaying future battles and increase the total number of casualties in order to achieve victory''. I'm not suprised at Blair decision. When we choose leaders, do we favor long term strategic thinkers, or opportunistic pretty faces?
kerblang 6y ago
They might as well have included the granddaddy example (as the age of computing goes): The vietnam war, mcnamara & body counts.
neonate 6y ago
https://archive.is/ynOm2 https://archive.is/ynOm2
foolinaround 6y ago
around paywall : https://archive.vn/ynOm2 https://archive.vn/ynOm2
jeffpeterson 6y ago
All observation is theory-laden; data cannot speak for itself.

The article seems to stop pretty early, as if something is missing. It’s an anecdote about a government incentive to have doctors see patients within 48 hours causing doctors to refuse scheduling patients later than 48 hours in order to get the incentive bonus. This is not an example of limits of data, but an example of perverse incentives.

deleted 6y ago
[deleted]
justinclift 6y ago
> It’s an anecdote about a government incentive to have doctors see patients within 48 hours causing doctors to refuse scheduling patients later than 48 hours in order to get the incentive bonus. That part is probably just the first 1/8th or so of the article (rough guess). Sounds like it was cut short for you?
jabroni_salad 6y ago
When a measure becomes a target, it ceases to be a good measure. This case could be said to be creating misleading data. If the doctor's offices aren't recording appointments more than 48 hours in advance, the System is losing visibility on the total number of people who want appointments. Every office will appear to be 100% efficient even though there is effectively still an invisible waiting list.
polytely 6y ago
Did you use reader mode? because I noticed that when I use firefox's reader mode it will cut off part of the article on the New Yorker site.
- strathmeyer 6y ago
  We clicked on the link and were presented with two paragraphs. New Yorker articles usually don't show up correctly, I don't know why they allowed to be posted here. Acting like we don't know how to read a webpage is gaslighting.
  - polytely 6y ago
    Well that wasn't my intention at all, just a problem i encountered a few hours before I read your comment so it was fresh on my mind.
- Fishkins 6y ago
  When I first opened the article in Firefox, I only saw the first two paragraphs (same as the OP). This was true whether it was in reader mode or not. I opened it in Chrome and saw the whole article. I just tried opening it in Firefox now (a couple hours later) and I see the whole article. If I switch to reader mode I do see it's truncated about halfway through, but I think that's a separate issue from what the OP was seeing.
- wodenokoto 6y ago
  On iOS. Initially used reader mode, but switched because it seemed cut off. But also without reader mode I can’t see more than the NHS anecdote.

carlosf 6y ago

I am increasingly worried with people applying ML in everything without any rigour. Statical inference generally only works well in very specific conditions: 1 - You know the distribution of the phenomenon under study (or make an explicit assumption and assume the risk of being wrong) 2 - Using (1), you calculate how much data you need so you get an estimation error below x% Even though most ML models are essentially statistics and have all the same limitations (issues with convergence, fat tailed distributions, etc...) it seems the industry standard is to pretend none of that exists and hope for the best. IMO the best moneymaking opportunities in the decade will involve exploiting unsecured IOT devices and naive ML models, we will have plenty of those.

CabSauce 6y ago
Wait until you find out low many studies have been published in medical journals with serious statistical flaws.
iagovar 6y ago
ML looks (for many peole) like a way to circunvent your grumpy statiscian saying that the underlying data is worthless and/or you should focus on getting the data pipeline done properly for a logit model on your churn rate.
- analog31 6y ago
  "Scientist free science," -- being able to optimize systems without understanding them, has been a dream of the business world since the dawn of time. There's always been a market for cookbook recipes that automate the collection of data, and interpretation of results. Before ML, there were "design of experiments," and "statistical quality control."
  - carlmr 6y ago
    >Before ML, there were "design of experiments," and "statistical quality control." Statistical quality control, at least the way I know it, is very useful in finding problems in your process. I'm also not sure how this fits with your premise. It's about optimizing systems by first finding out where to look, and then looking there in detail with expert knowledge, i.e. deep understanding of your system.

canadianwriter 6y ago

Data always needs to be paired with empathy. ML/AI simply doesn't have empathy so it will always be missing a piece of the overall pie. Let AI crunch the numbers, but combine it with a human who can understand the "why" of things and you can really kick butt.

gnulinux 6y ago
I agree with you, although, unfortunately, most -- if not all -- engineers I know would respond to this by complaining about how "a human who can understand the 'why'" cannot be automated.

sradman 6y ago

From the ungated archive [1]: > Whenever you try to force the real world to do something that can be counted, unintended consequences abound. That’s the subject of two new books about data and statistics: “Counting: How We Use Numbers to Decide What Matters”, by Deborah Stone, which warns of the risks of relying too heavily on numbers, and “The Data Detective”, by Tim Harford, which shows ways of avoiding the pitfalls of a world driven by data. Data is a powerful feedback mechanism that can enable system gamification; it can also expose it. The evil is extracting unearned value from a system through gamification not the tools employed to do so. I’m looking forward to reading both books. [1] https://archive.is/ynOm2 https://archive.is/ynOm2

kkoncevicius 6y ago

This article is not so much about the data, as it is about rules and thresholds used to divide that data into groups.

mdip 6y ago

Kind of love the initial story in the article about 48-hour wait times. I had a stint writing conferencing software for quite some time, and every once in a while we'd come across a customer requirement that had capabilities which were obvious to us developers "would be misused". As a result, we did the "Thinking, Fast and Slow" pre-mortem to help surface other ways that the system could be attacked (along with what we would do to prevent it and how it impacted the original feature). If you create something, and open it to the public, and there's any way for someone to misuse it for financial incentive (especially if they can do so without consequence), it will be misused. In fact, depending on the incentive, you may find that the misuse becomes the only way that the service is used.

kazinator 6y ago
When the doctor's office is inundated with patient visits, you cannot fix scheduling back-logs by fiddling with the scheduling algorithm, no matter how much data you have. Say the calendar is initially empty and 1000 people want to see the doc, right now. You can fill them all into the calendar, or you can play games that solve nothing, like only filling tomorrow's schedule with 10 people, asking 990 of them to call back. That doesn't change the fact that it takes 100 days to see 1000 patients. All it does is cause unfair delays; the original 1000 can be pre-empted by newcomers who get earlier appointments since their place in line is not being maintained.
lifeisstillgood 6y ago
conferencing as in 'ComicCon' or 'Zoom'? Can you give an example?
- mdip 6y ago
  Haha, hadn't even thought of that -- Conferencing as in developing bespoke (and some white-label) software for organizations deploying Office Communications Server (and R2), Lync and ultimately Skype for Business (I do a little Teams work these days but I am focused on other areas, presently).
strathmeyer 6y ago

lmm 6y ago

Does the use of statistics actually amplify misunderstanding, or merely reveal misunderstandings that were already there? In any of these examples given - predicting rearrests, infant mortality, or so on - it's hard to imagine that someone not using numbers would have reached a conclusion that was any closer to the truth. Data has its limits, but the solution is usually - maybe even always - more data, not less.

mjburgess 6y ago
It's pretty trivial to predict things without "data". Data just means using some measurement system to obtain measurements of some target phenomenon. Many targets cannot be measured, or have not occurred to be measured. Reasoning counter-factually is trivial: What would happen if I dropped this object in this place in which an object, of this kind, has never been dropped before? Well apply relevant models, etc. and "the object falls, rolls, pivots, etc.". This is reasoning-forward from models, rather than backwards from data. And it's the heart of anything that makes any sense. Data is not a model and provides no model. The "statistics of mere measurement" is a dangerously utopian misunderstanding of what data is. The world does not tell you, via measurement, what it is like.
- lmm 6y ago
  But where does that model come from if not from data? We might use some logical principles to inform our model - but don't those principles themselves also ultimately have to be inferred from data about the world?
  - mjburgess 6y ago
    measurement of our bodies engaged in deliberate action measurements of the world resolve ambiguities; they do not 'contain' descriptions of the world, not can they provide any measurements of objects must be interpreted by models

visarga 6y ago

Data is very limited, indeed. We can't predict outside the distribution, or unrelated events (without a causal link), or random events in the future. We should be humble about the limits of data.

_rpd 6y ago
Sure, but coding a human-equivalent response to such events is trivial (because no one and nothing responds well to such events).

Ozzie_osman 6y ago

Data is not a substitute for good judgment, for empathy, for proper incentives. The article focuses on governments and bureaucracies but there's no better example than "data-driven" tech companies, as we A/B test our key engagement metrics all the way to soulless products (with, of course, a little machine learning thrown in to juice the metrics). I wrote about this before: https://somehowmanage.com/2020/08/23/data-is-not-a-substitute-for-good-judgment/ https://somehowmanage.com/2020/08/23/data-is-not-a-substitut...

mminer237 6y ago
I think it's almost worse in tech because it largely works. If the government sets a flawed metric, their real goal of pleasing their constituents has failed and theoretically they either have to fix it or lose political support. But in tech, if your goal is just to make money, soullessly following data will often get you there, to the detriment of everyone else. Clickbait headlines will get you more views. Full-page popup ads will get you more ad clicks/newsletter subscriptions. Microtransactions will get you more sales. Gambling mechanics will get you more microtransactions. You can say it's a flawed metric, but I think in the end, most people just actually care more about making money than they do about building a good product.
gen220 6y ago
I've written the same sentence before! This is so cool! pardon the wall of text. Here's my thesis, curious to hear your thoughts. At some time around 2005, when efficient persistence and computation became cheap enough that any old f500-corp could afford to endlessly collect data forever, something happened. Before 2005, if a company needed to make a big corporate decision, there was some data involved in making the decision, but it was obviously riddled with imperfections and aggregations biases. Before 2005, executives needed to be seasoned by experience, to develop this thing we call "Good Judgement", that allows them to make productive inferences from a paucity of data. The Corporate Hierarchy was a contest for who could make the best inferences. Post-2005, data collection is ubiquitous. Individuals and companies realized that you don't need to pay people with experience any more, you can simply collect better data, and outsource decision-making to interpretations of this data. The corporate hierarchy now is all about how can gather the "best" data, where "best" means grow the money pile by X% this quarter. "Good Judgement" used to be expected from the CEO, down to at least 1-3 levels of middle management above the front-line people. Now, it appears (to me) to be mostly a feature of the C-Suite and Boards, and it's disappeared elsewhere. Long-term, high-performing companies seem to have a more diffused sense of good judgement. But these are rare. maybe they always have been? Anyways, as we agree, this has a tendency to lead in problematic directions. Here's my thesis on "why". Fundamentally, any "data" is reductive of human experience. It's like a photograph that captures a picture by excluding the rest of the world. Few people seem to understand this analogy, because they think photographs are the ultimate record of an event. Lawyers understand this analogy. With the right framing, angle, lighting (and of course, with photoshop), you can make a photograph tell any story you want. It's the same issue with data, arguably worse since we don't have a set of standard statistics. We have no GAAP-equivalent for data science (yet?). Our predecessors understood that data was unreliable, and compensated for this fact by selecting for "Good Judgement". The modern mega-corps demonstrate that we don't have a good understanding of this today, evidenced by religious "data-driven" doctrine, as you describe. People will say "hey! at least some data is better than no data!", to which I'll say data is useless and even harmful in lieu of capable interpreters. In 2021, have an abundance of data, but a paucity of people who are capable of critical interpretation thereof. I don't know if it's a worse situation than we had 20 years ago. But it's definitely a different situation, that requires a new approach. I think people are taking notice of it, so I'm hopeful.

kazinator 6y ago

> doctors would be given a financial incentive to see patients within forty-eight hours. Not measuring that from the first contact that the patient made is simply dishonest. "Call back in three days to make the appointment, so I can claim you were seen within 48 hours, and therefore collect a bonus" amounts to fraud because the transaction for obtaining that appointment has already been initiated. I mean, they could as well just give the person the appointment in a secret, private appointment registry, and then copy the appointments from that registry into the public one in such a way that it appears most of the appointments are being made within the 48 hour window. Nothing changes, other than that bonuses are being fraudulently collected, but at least the doctor's office isn't being a dick to the patients.

Eridrus 6y ago
It's really hard to design a not game-able metric. The problem here seems to be that doctors are under-provisioned for some reason, and so long wait times are a form of load shedding for the system. Without addressing this core issue, which individual clinics have little control over because they are generally boxed in by regulations over who can administer medical care, except to rush appointments (which they're probably already doing), there's not much they can do to solve the problem, so all they can do is try to game the rules or not get the bonuses.
- deleted 6y ago
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- closeparen 6y ago
  Doctors don’t get to bill for idle time. Being less than fully utilized is leaving money on the table. The idea here is presumably to compensate them for leaving gaps in their schedules.
  - Eridrus 6y ago
    What you're describing is effectively what doctors did: they left their entire calendar free until the last moment and only took appointments then. It turns out this is not actually what people want; people want this availability to exist, but also do not want to be turned away if they book ahead of time, which points to this being a capacity problem, not a scheduling problem.

williesleg 6y ago

solve covid, not a vaccine but a treatment, those are forbidden.

ppod 6y ago

This author has published a couple of articles like this at the New Yorker They all have this in common: the author works through some interesting and in some ways unusual cases where data or statistics have been improperly or naively applied, with some social costs. I really enjoy the articles themselves. Then the New Yorker packages it up with a cartoon and a headline and subheadline like "Big Data: When will it eat our children?" or "Numbers: Do they even have souls?", and serves it up to their technophobic audience in a palatable way. https://www.newyorker.com/contributors/hannah-fry https://www.newyorker.com/contributors/hannah-fry

dsaavy 6y ago
> *Numbers don’t lie, except when they do. Harford is right to say that statistics can be used to illuminate the world with clarity and precision. They can help remedy our human fallibilities. What’s easy to forget is that statistics can amplify these fallibilities, too. As Stone reminds us, “To count well, we need humility to know what can’t or shouldn’t be counted.”* I do have a problem with her conclusion here. Are numbers really lying if it's actually an incorrect data collection method or conflicting definitions of criteria for generation of certain numbers (like the example used in the second to last paragraph)? She seems to be pointing out a more important fact, which is that people don't question underlying data, how it was collected, and the choices those data collectors made when making a data set. People tend to take data and conclusions drawn from it as objective realities, when in reality data is way more subjective.
- lupire 6y ago
  > Are numbers really lying if it's actually an incorrect data collection method or conflicting definitions of criteria for generation of certain numbers Obviously it's a figurative metaphor, but it's pretty clearly a case of "this supposedly objective factual calculation is presenting an untruth."
  - Nasrudith 6y ago
    You can still be very misleading with objectively true calculations. "There is very low stress on the patient's arteries and only a very small tear." - said patient bled to death and has only atmospheric stress now that their veins are bloodless. Less than 0.1% of their total vein area has a rip in it.

tppiotrowski 6y ago

"once a useful number becomes a measure of success, it ceases to be a useful number" Two other unintended consequences of incentives I learned in economics: 1. Increasing fuel efficiency does not reduce gas consumption. People just use their car more often. 2. Asking people to pay-per-bag for garbage pickup resulted in people dumping trash on the outskirts of town. Edit: Did more research after downvote. Definitely double check things you learn in college 1. The jury is still out: https://en.wikipedia.org/wiki/Jevons_paradox https://en.wikipedia.org/wiki/Jevons_paradox 2. Seems false https://en.wikipedia.org/wiki/Pay_as_you_throw#Diversion_effects_and_risks https://en.wikipedia.org/wiki/Pay_as_you_throw#Diversion_eff...

tomrod 6y ago

An absolutely fantastic article that captures my concerns as a user, purveyor, and automater of systems that help with numbers. I'm always very cautious regarding the jump from numbers informing to numbers deciding.

temp8964 6y ago

This article is totally gibberish. It's a terrible mixture of many unrelated things. Just because those things all have something to do with data (anything can be presented in numeric form), it does not make their issues are about data. First, the Tony Blair example is not about data. It is a failure of government planning. It's wrong politics and wrong economy. The G.D.P. example is laughable. G.D.P. is never intended to be used to compare individual cases. What kind of nonsense is this? And the IQ example. The results are backed by decades of extensive studies. The author thinks picking a few critics can invalidate the whole field. And look! The white supremacist who gave Asians the highest IQ, what a disgrace to his own ideology. Many more. I feel it's kind of tactic to produce this kind of article. Just glue a bunch of stuff, throw together with somethings seem to be related, bam, you got an article.

cloogshicer 6y ago
Gotta disagree here. This article is acknowledging a pattern, that data is misused in many different areas. I think the problem goes even deeper, which is a misunderstanding of the scientific method. Good discussion about this topic here: https://news.ycombinator.com/item?id=26122712 https://news.ycombinator.com/item?id=26122712
- temp8964 6y ago
  "data is misused in many different areas" is not a valuable / informative point. There are many wrongs seem to have something to do with data, but in fact they are not. Like socialist economy planning will eventually fail, but then you would say they misused data. It seems relevant, but misusing the data is not the real cause of their failure at all.
  - not2b 6y ago
    Replace "socialist" with "large company". The companies gather data, establish metrics, and manage to those numbers, and often bad things result. Ever been in a company where some internal support function goes to hell because its top manager's bonus depends on a metric, and they can improve that metric by refusing to support the users (find excuses to close IT support calls without fixing the issue, etc).

jplr8922 6y ago

Confusing performance metrics and strategical objective is not a data problem, it is a human problem. It happens to a lot of people outside the usual Blair-WhiteNationalist-IQ crowd. I do not think that advanced technical knowledge in ML or stats is required to avoid this mistake ; it is the ability to perform valid counterfactuals statements. A good example of what I mean can be found on wikipedia : His instinctive preference for offensive movement was typified by an answer Patton gave to war correspondents in a 1944 press conference. In response to a question on whether the Third Army's rapid offensive across France should be slowed to reduce the number of U.S. casualties, Patton replied, "Whenever you slow anything down, you waste human lives."[103] https://en.wikipedia.org/wiki/George_S._Patton https://en.wikipedia.org/wiki/George_S._Patton Here, US general Patton is not confounding a performance metric (number of casualities) with strategic goal (winning the war). His counterfactual statement could be that ''if we slow things down, you are simply delaying future battles and increase the total number of casualties in order to achieve victory''. I'm not suprised at Blair decision. When we choose leaders, do we favor long term strategic thinkers, or opportunistic pretty faces?

kerblang 6y ago

They might as well have included the granddaddy example (as the age of computing goes): The vietnam war, mcnamara & body counts.

neonate 6y ago

https://archive.is/ynOm2 https://archive.is/ynOm2

foolinaround 6y ago

around paywall : https://archive.vn/ynOm2 https://archive.vn/ynOm2

jeffpeterson 6y ago

All observation is theory-laden; data cannot speak for itself.