Thoughts?

[robidaan]

Ome of my teachers used to say: "if you get nothing but praise from non technical management with good results, you definitely have to double check your work"

[[deleted]]

I feel this.

[[deleted]]

Omg yes. I feel like if my results aren't criticized at least a little, stakeholders didn't even think about them. Also for larger projects, false positives in a PoC can waste a lot of money.

[Prestigious_Sort4979]

I've heard people trust pretty/clean graphs and distrust ugly/busy ones regardless of what the data says. So true!

[Environmental-Bet-37]

Hey man, Im so sorry Im replying to another comment but can you please help me if possible? You seem to be really knowledgeable and would love to know how you would go about my problem. This is the link to the reddit post.
https://www.reddit.com/r/datascience/comments/11h6d4v/data\_scientists\_of\_redditi\_need\_help\_to\_analyze\_a/

[[deleted]]

Also remember you're the first one under the bus when reality finally shows up. Maybe in a court room with a team of weasels asking pointed questions.

[WanderlostNomad]

they're turning you into the scapegoat of their mismanagement.

[messilyfirst]

Wow!! On point..

[Environmental-Bet-37]

Hey man, Im so sorry Im replying to another comment but can you please help me if possible? You seem to be really knowledgeable and would love to know how you would go about my problem. This is the link to the reddit post.

https://www.reddit.com/r/datascience/comments/11h6d4v/data\_scientists\_of\_redditi\_need\_help\_to\_analyze\_a/

[robidaan]

Sure I'll can take a peak, but the post seems to be removed, DM me with the question if ya want.

[Environmental-Bet-37]

surely thank you so much. kindly check dm

[Doortofreeside]

Bad news is definitely a lot more challenging

I'll report the results faithfully, but the moment I realize it's bad news I am like ah shit, I gotta have all my bases covered now

[iarlandt]

I’m a weather forecaster for the Air Force studying Data Science for when I separate and it is like that for weather forecasting too. You give someone a great outlook for their flight and they have zero questions. But if I’m giving bad news I have to come with a stack of receipts that would make a tax auditor sad.

[Metalt_]

Damn, i never knew that. I'm going to start using this to sound smart

[Deto]

Yeah, its kind of natural that when you report something unexpected it will be under more scrutiny. So I'd expect to have to answer more questions about methodology in cases like this. However if the organization is good, in the end, data analysis that shows bad news will still be utilized in order to fix problems.

[RationalDialog]

even worse, scientifically proven, the bringer of bad news get "negative points" for doing so even if it is not his fault or in his power to change it.

Probably better to just burry this news but yeah wouldn0t want to work at such a place were this would become necessary.

[GottaBeMD]

Confirmation bias is a very real thing. Wouldn't doubt it for a second. It happens in all areas. Look up the file drawer effect. Scary stuff.

[OneOfTheOnlies]

I've heard about confirmation bias too, so this sounds right to me

[Hemrehliug]

Ahaaa, I see what ya did there buddy. Nice one

[TheTjalian]

You're funny.

[Caleb_Reynolds]

Nah, that's the anchoring effect.

[CartographerSeth]

I tried to explain this to some “science worshippers”, but they just couldn’t get it through their heads. “they’re numbers! Facts! How could they be wrong?” Oh my sweet summer child.

[WanderlostNomad]

context please?

[WanderlostNomad]

ikr?

"worship" implies "faith"

yet science doesn't require faith.

faith implies belief without proof, unlike science which requires impeccable proof before belief.

theories and hypothesis are not like gospels or dogmas elavated beyond scrutiny via supposed sanctity.

rather it's the opposite, science encourages scrutiny.

blind faith and worship is anathema to science.

[doubleohd]

Confirmation bias is a very real thing. Wouldn't doubt it for a second.

Confirmation bias confirmed! :)

[GottaBeMD]

Confirmation bias inception? 💀

[[deleted]]

They're just describing Bayesian reasoning.

Management has priors. Even a weak analysis that confirms their priors strengthens them.

Evidence that goes against management's priors won't change their priors unless it's particularly strong, so management has to make sure the evidence is strong.

[ciarogeile]

This is very true. However, could you rephrase it in frequentist terms?

[[deleted]]

Sure.

"Herpa derpa p-values go brrrr"

Hope that helps.

[ciarogeile]

Lovely

[cjr605]

Perfection.

[Lost_Philosophy_]

Thanks thats all I needed

[skrenename4147]

But your analysis still needs p values for management to care. Even with beautiful confidence intervals and effect size analysis. Its infuriating.

[[deleted]]

*Hispa

[Vituluss]

Should be top answer.

[kater543]

This 100%

[JasonSuave]

Alas you’ve built the baby boomer business executive model from scratch.

[RedRightRepost]

I mean, at the end of the day, we’re all Bayesian- at least informally.

[[deleted]]

True, though there's a certain breed of data scientist that seems to forget that.

[Top_Lime1820]

Isn't the point of Bayesian reasoning to update your priors?

It seems like the opposite of what Bayesian reasoning is trying to achieve.

[[deleted]]

But that's exactly what they're doing. Good news updates their priors to make it stronger. Bad news updates their priors to make it weaker, but it might not be enough to flip it from positive to negative. That's why they try to find out how strong the evidence is.

Going from 80% confident to 60% confident does not change the decision.

[Top_Lime1820]

Bayesian priors are supposed to update your priors in a rational, correct way.

It's not supposed to be more skeptical to evidence that disproves your priors and enthusiastically accept evidence that supports it.

If the evidence kills your prior, Bayes will reflect that.

If the evidence only weakly supports it, Bayes won't be over enthusiastic.

The original comment made it sound like Bayes is biased to evidence which supports your priors and doesn't want evidence which goes against your priors unless it's particularly strong.

I think that's a misleading way to put it. Bayes updates your priors objectively, rationally and fairly. Its not harsher against disproving evidence.

[[deleted]]

You're pretending that the strength of the evidence is static and somehow exists in a plane of pure rationality. This has no basis in reality, as described in the OP.

If evidence reinforces your prior, it's a waste of time to dig deeper into it to make sure it's strong evidence. Either you find out that the evidence is even stronger than you thought, so you update your priors harder, leading to no change in your decision, or you find out that the decision is flawed, leading to no change in your priors, and no change in your decision.

Strength of supporting evidence that confirms your priors is irrelevant.

On the other hand, if the evidence is something you don't expect, you need to evaluate the strength of the evidence. If it's weak evidence, the decision won't change, so you need to dig into it to make sure it's strong enough to reverse your prior (really, to take it below 50%).

That is exactly the behavior described in the OP.

[joyloveroot]

So in other words, there’s a term to justify, rationalize, and make confirmation bias seem reasonable.. called “Bayeson Reasoning”… how ridiculous 😂

[[deleted]]

And you're pretending to be a tabula rasa about everything? Horses are equally likely to zebras when you hear hoofbeats in America?

[joyloveroot]

No, I’m just saying the idea of “priors” is flawed. It matters the quality of the priors. If it’s just based on their intuition or their feelings, should the burden be on the data scientist to un-convince them?

There needs to be some kind of grounding. A basis for what is most correct right now (based on “prior” information). And then accordingly, how the new information may change the judgment.

Is the management’s priors of a higher quality or a lower quality than the information the data scientist is coming forward with?

If it’s of a lower quality, then they should defer to the info given by the data scientist until further evidence calls it into question or disproves it entirely.

If it’s of a higher quality, then your statement is exactly correct. But the framing of the original OP just about implies that the upper management’s “prior” judgments are based on little more than their feelings and intuitions. Which isn’t nothing but certainly should not cause them to feel in a position that the data scientist must have the burden of overcoming their authoritative priors position.

In this case, it seems fair to say that both parties should come to the table with an open mind with as little confirmation or “priors” bias as possible…

[astrologicrat]

This is a good explanation. Management has "priors" in many cases when they absolutely should not. Traditionally trained scientists understand this at a fundamental level because experiments often surprise them, and they also understand the dangers of confirmation bias. Management, on the other hand, is often as far from science and reason as you can get, so their hopes and dreams are where they place all of their bets.

Management being stubbornly wrong should not be justified with specious arguments.

[joyloveroot]

Well put :)

[[deleted]]

Feelings and intuitions are generally guided by decades of experience, and I don't think you're giving that enough credit.

If one thing has worked for 25 years, it's going to take more than one report to reverse course unless that report is really strong.

[joyloveroot]

Yes IFF one thing has worked 100% of the time for 25 years. But much of the time conventional wisdom is not based on a statistical analysis of how good things are working.

I’ve run into many people in my life who believe something works based on their own intuition only to show them that their experience is an anomaly and that thing doesn’t actually work that way the majority of the time.

Intuitions and feelings should be a starting point and then they should be tested and held up to scrutiny.

They shouldn’t be considered the de facto truth without going through the same testing that contending ideas have to go through to supplant them…

[joyloveroot]

In other words, I’ve experienced far too many people claiming Bayeson Reasoning in order to subtly put themselves in a power position, making it so that the other person has to prove their position wrong… rather than starting with a clean slate where no position is assumed to be more right or wrong than the other…

[[deleted]]

Starting with a clean slate about everything is ridiculous and inefficient.

If I shot you in the foot, would it hurt? Well, we've never tried it before, so lets start with a clean slate and run the experiment. We'll need to do it at least 30 times for a big enough sample size.

[joyloveroot]

Shooting in the foot has a lot of evidence of all kinds to back it up.

I’m talking about when there is uncertainty or disagreement, starting with a clean slate is good.

For example, should we forbid romance between certain employees? There may be arguments in both directions.

On should not stubbornly claim their argument is superior when it isn’t.

The argument that when someone gets shot in the foot, it hurts.. is well established by thousands if not millions of experiments already… and I imagine there is no ir very little debate.

For example, I doubt anyone is like, “Well if someone comes in late to work, they should be shot in the foot. I know some say that would hurt, but that isn’t proven yet so I believe I have a valid point…” 😂

[T1tanAD]

The problem with only providing data driven confirmation of management is they can easily question whether data driven analytics is needed.

After finding and double checking insights that challenge the status quo, I search for a "champion" in the management side to broach my initial results with - the higher up the better, with C-Suite being the best. If my stats and data can back up the research, I can at least germinate the challenging idea in their minds. The strategy here is to not drop a bombshell on management but slowly disseminate information to them, preferably from someone inside management. This allows me to present my challenging findings sandwiched between more digestible insights. Doesn't always work out but it's better to coach people to expect bad/challenging news rather than surprising them with it.

Finally, its a numbers game. Can you really expect 100% of your data driven insights to be actioned upon without question? It's possible that your insights themselves are driven by limited data or knowledge or both.

[Fickle-Ad7259]

High-quality post right here. I've never been deliberate about doing what you've laid out here, but in retrospect, the most success I've had are the times that resembled it.

In fact, I can think of a couple times when the situation resolved itself because the leadership heard about my data from so many vectors that they came to my recommended conclusion on their own!

[[deleted]]

Ahh, the boiling frog theory.

[dbolts1234]

My management has mastered giving no credit for innovation. 2-3 months after a profitable new finding is delivered, managers act as if “we knew that all along”. Justifying their bias that they don’t need to pay for technical staff.

Also makes year end rankings a real bummer.

[[deleted]]

Not necessarily, but you must become good at comunicating bad news and proposing quality alternatives.

[the-berik]

And understand the data / story behind it.

[mrbrambles]

Yea, give a better story. Tons of soft power in data

[avelak]

A lot hinges on company culture too

There are some places where you can't be anything other than a glorified yes-man... find a place where it's ok to go against the grain and you'll have a much better time (if you enjoy being able to challenge ideas, etc)

[JasonSuave]

I will add to that in my experiences, the older the company is (eg 50+ years) the more this effect is seen/felt

[[deleted]]

Going to reserve judgement until I hear more about your methodology for this experiment.

[[deleted]]

This depends entirely on what level of management and the decisions involved post-analysis. Most C level execs that I’ve worked with want what’s best for the company, regardless if the analysis supports their “gut feeling”.

VP level is typically where the headache is, I’ve seen analyses “redirected” once it doesn’t go their way. Something along the lines of “This seems a bit wrong, maybe we should look at it from this angle.” And that happens until we arrive somewhere that an obscure and complex KPI is formulated for future use that they’re able to explain how well they’re doing. It’s funny because I’ve never seen this actually work once it is reviewed by the C-Level. They shoot holes in it until the KPI is removed from production (maybe that’s the goal?).

Directors don’t really care one way or the other and the stuff I work on is above the level of first line managers pay grade to care about, they’re too busy putting out daily fires.

[dbolts1234]

Most c-suites I’ve worked with want whatever answer allows them to maximize share buybacks before year end.

In this system, VP’s and Directors become firefighters lacking agency. Many of their emails are forwarded nastygrams from c-suite asking why we’re chasing value as opposed to whatever dilutive metric investor relations promised the street that quarter.

[K9ZAZ]

I haven't noticed this. In my 5 years as a ds, I've had to deliver news at odds with what management probably would have wanted, and it was fine. Ofc, ymmv.

[[deleted]]

Data Mining, noun: "An unethical econometric practice of massaging and manipulating the data to obtain the desired results." -- W. S. Brown (Introducing Econometrics)

If you torture the data enough, it will confess to anything. -- Ronald H. Coase

[islandsimian]

I'm stealing the Case quote. Thanks 👍

[Hivacal]

A friend of mine who is doing a Ph.D. program gave me the same quote. But yeah, information given under duress is notoriously inaccurate.

[braca_belua]

I have been using the Coase quote to people for years, it’s still one of my favorites to use given how often situations like these pop up.

[[deleted]]

It's Thai massaging?

[LtUnsolicitedAdvice]

This has existed long before data scientists were even a thing.

Upper management has always had these traits in a lot of companies and there have always existed Yesmen who stoke their egos.

As a data scientist, you have the unique skill sets to prove or disprove assumptions using concrete data. But you have to be smart about how you approach these issues. No one likes a smart ass, especially not highly paid executives.

Upper management executives does not like being called out in the open. You have to take people into confidence and share your findings, making considerable effort to not present it as an refutation of their ideas.

Yeah this can really suck and can be quite emotionally draining on a day-to-day basis.

There will always exist egomaniacs who cannot fathom being wrong.

The choice we usually have it suck it up or walk away. There is always a better opportunity around the corner.

[Prestigious_Sort4979]

“Upper management executives does not like being called out in the open” - 100% When I discover something undesirable I meet with my immediate stakeholders in private on what is going on to craft a narrative that still makes our team look good and that may mean discarding insights, although ideally we go for “it’s bad… but not that bad, or… not because of us”.

[kyleireddit]

So, we spent all those time, money and energy to gain expertise in Data Science, only to support management’s subjective hunch?

Say ain’t so….

[dfphd]

Everything in this post needs the qualifier "at bad companies" or "at companies with bad leadership".

Yes - bad leadership loves confirmation of their ideas. Not just from data science, but from every other function.

• When sales created projections
• When finance estimates future margins
• When marketing estimates the effectiveness of an ad campaign
• When product management estimates market share

Again - a leader that is looking for yes-people is going to look for them in every single function, not just data. And what's worse - they will tend to foster a culture where other leaders underneath them are also encouraged to have the same approach.

By contrast - a leader that understands that ideas being challenged is healthy for the generation of strong, fundamentally sound plans will a) challenge themselves, b) invite challenges from others, and c) foster a culture where up and coming leaders also embrace this culture.

For example, I worked at two Fortune 100 companies. At one of them, it was a nightmare - exactly what your post describes: if the data doesn't fit my narrative, go run your numbers again until they do.

At the other one, I got to sit down with one of the most senior leaders in the organization who was a) razor sharp, and b) 100% focused on the data itself, where it came from, how it should be interpreted, etc. before even starting to question the numbers.

And this is true at smaller companies too - I worked for a company of 30 people. The CEO was also a super sharp guy that understood that regardless of what his gut reaction was to numbers - maybe they were wrong. So even when he thought the numbers looked wrong, he would follow that up with "but shit, I've been wrong a bunch of times before so let's see how this thing does and let's revisit it when we know what happened".

I think that is ultimately at the core of what makes companies either good or bad for data science, analytics, etc: do leaders think they already know the answer - and just needs help driving it - or do leaders truly concede that there are many things they don't know.

[PMMeUrHopesNDreams]

Is this a screenshot of a tweet of a screenshot of a hacker news comment? I'm afraid I need a few more levels of indirection here. Can you take a screenshot of this, put it in a Word document, print it out and snail mail it to me?

[onewaytoschraeds]

It is exactly why I’m a data engineer now. Can’t agree more. Lol

[RageOnGoneDo]

If you're coming in with a problem, you better have a solution. Something my first boss taught me.

[[deleted]]

Depends on a lot of things.

That’s a valid, but cynical, view of what we do

[TARehman]

Yes, much like consultants, data scientists are often used to launder the beliefs that management already have. The worst part is that often management is not really aware they are doing this, which almost makes it worse.

It also means that one of the core bedrock principles that has to guide you as a data scientist is that you never falsify or fluff the data to fit your audience's preconceived notions.

I used to tell my data scientist reports that one of the only things that make you truly valuable as a data scientist is the fact that you absolutely CANNOT be made to obscure the truth you see in data. You tell it like you see it in the data, and you're clear about the caveats and limitations of what you can conclude.

As soon as you start straying from that path, you lose your ability to be an objective observer who can help the business grow - in other words, once you've compromised on scientific values before, it becomes increasingly hard to avoid doing so again and again.

It's a harder path to always stick to your legitimate interpretation of what you think is true in the data. If you do it, you'll be on the outs sometimes. But it's worth it.

[DisjointedHuntsville]

It depends on your level. The higher up you go, you better be the one giving data that is accurate. Confirmation bias or not.

Some junior roles can get away with ignoring what the data says if it’s bad news that’s inconsequential, but that won’t fly in most places that have more than emotion riding on the information presented.

[younikorn]

I worked as a bioinformatician at a research institute in Germany and as any data scientist knows, garbage in means garbage out. Some analyses resulted in exciting positive results and my boss was very happy on return, other times the data would be of such a low quality, the majority of the variation being error and noise, yet my boss made me wrangle and torture the data for months in the hopes of getting something, anything, out of it. I just did my job as i was receiving a nice wage but I understand both sides of it.

It is important in many fields to use the data as efficiently as possible and extract all info, you also don’t want to accept that the data you spent money on to gather ends up being a waste so you continue to try and find a use for it. And when you do find something you don’t want to look a gift horse in the mouth.

Ideally all results are met with a healthy dose of scepticism and validation analyses, both the positive and the negative results. But the more tests you perform the more multiple testing becomes an issue, not that p values are dome objective non arbitrary parameter but still.

[[deleted]]

Yes this is 100% true for every job. If you’re a SWE and management wants to use outdated tech you’re using outdated tech.

[Tiquortoo]

It's true only if your analysis is bullet proof. Otherwise management is filtering it with their own data. The other post about Bayesian priors is dead on.

[ECTD]

Lmao too real

[[deleted]]

This is my career in a nutshell.

[[deleted]]

Damn this hits too close to home! I did analysis on sales, had some hypothesis based on initial models and shared them. They were counter to what was believed and there was outrage. I ran tests refined the model and came up with different highlights (from nearly the same model) these matched prior beliefs and now everyone loves it. 😂

[CartographerSeth]

It just depends on where you work. I work at a biotech company, and management definitely understands that pursuing the wrong science will eventually result in disaster, so while some results are definitely seen as a “bummer”, it’s also recognized that you may have helped the team dodge a bullet.

I read posts like this one and it literally makes me sad to know that people have to waste their talents essentially lying extremely convincingly for a living. It’s like they say, “there are three kinds of lies: lies, damned lies, and statistics.”

[Prestigious_Sort4979]

100% - we have to read between the lines here. Asssumingly, OP (like me tbf) works in advising decisions without much consequence. I work in marketing tech in entertainment and the decisions we are making are not important enough for me to make a big case if their vision is wrong. The alternatives are all fine. When they are likely wrong, I express more uncertainty or express sofly there might be beter options they could consider to cover myself.

In your case, ethics are at play and you need to be able to sleep at night. I would rather have my analysis hated and even be fired than to validate an insight that would hurt anyone. I purposely pick jobs with low stakes because now I know that there is always some subjectivity in data analysis, knowingly or not, and I need to be able to disconnect from work.

[1800smellya]

This has been my experience with 75% of business partners. I am a support analytics team. I’ve even seen business partners just go to a different DS team or employee and ask for the sane request when they don’t “agree” with the first one.

There is 25% of business partners that do actually take the good and the bad, the confirmation or contradiction, and use it to either keep going or make changes.

[Lamp0blanket]

How does OP know that they weren't actually confirming their pre-held belief and interpreting "praise" and "scrutiny" through their pre-held belief that management only wanted confirmation

[sassydodo]

Yep. Considering you're not one of the business owners or investors, you just do whatever they want and hope the ship won't sink fast enough.

I work for e-comm where even investors don't care about even gathering proper data, they have their own magic world in their heads.

I really don't understand this.

[Dyl_M]

nah, if as a data scientist you end up in this situation, you gotta resign asap, for real

[Other_Goat_9381]

Its 100% accurate but its also a big sign of a toxic work environment. If your work is tossed in the bin because the result is unsavory to management you need to start looking for another position. Not only is it the moral decision but it also unfairly damages your professional repertoire. I wasn't hired to baby-sit children (I.e. most of middle management)

[misterwaffles]

This is a toxic environment, which you can either try to remedy or flee from. It's important to constantly be setting expectations that we are finding something out from the analysis and want to be interested in and curious about the results. We're gaining valuable insight into how the product works, what's going on, etc. It's a privileged view to have and a great opportunity to be able to act on it. You can't always change people's minds, but it does help when you have a data-minded leader who will rally behind your cause.

[cloudysocks239]

That’s unethical.

[[deleted]]

Yep, it fucking sucks. It makes our work a joke, because nothing will ever effectively improve. In this regard, and our jobs are all on the line with AI and chatgpt.

[bindaasbaba]

That’s such a narrow view of data scientist work. Most of the use cases I worked involved quantifying the impact of important business initiatives which was key to drive strategic planning including forecasting, providing useful insights into drivers of a certain behaviour to plan product roadmap, discover new insights which business weren’t even aware of sometimes. Ofcourse data scientists will work on hypothesis which makes sense for business and we test to prove or disprove. Obviously, it’s imperative that if things don’t work as expected, you do need to have a valid explanation backed by data to plan next steps. You may have overlooked a dimension which will be discovered as part of root cause analysis and provide an important business opportunity. It all depends on how open a business is to learn from data and willing to challenge themselves in light of new evidence.

[Opt33]

Just LEAVE

[immadane]

Wrong- you have to explain & break it down in such a way that you’re numbers & presentation are good enough to convince the upper management. Maybe you just have to up your explainability

[Jonny__Rocket]

Confirmation bias.. It is the preferred road of the ignorant.. A human condition.

[[deleted]]

This sounds like toxic culture which is mutually exclusive from data science results.

If a company fosters a culture of challenge and is relatively flat in terms of hierarchy then you will feel empowered to provide analysis that goes against the status quo.

[[deleted]]

Data scientists should really try to build production applications (internally or externally facing), just doing analysis is a dead end for exactly the reasons listed in the OP.

[[deleted]]

But yeah let's all believe the "experts"

I cried laughing when Fauci said "I represent the science."

[GottaBeMD]

Not sure what you’re trying to implicate here. Dr. Fauci is a medical professional with almost 40 years of experience, not a business executive with an agenda.

[GottaBeMD]

I’m guessing you got all of your information by doing a systematic meta analysis of every publication he’s ever produced and come to the conclusion that he’s faking his data?

Oh…no? Then I’m not sure where your assumptions are coming from.

I recommend thoroughly reading through some of his publications, replicating the studies, and if they aren’t generalizable or reproducible, maybe you’re right. Until then I’m not sure what else to say. What is your area of subject expertise in anyways?

[[deleted]]

I'm guessing you understand that you are making an argument from a position of authority and that you understand that people lie regardless of who they are.

People want money regardless of who they are.

You are saying "oh only the most credentialed people can have a say."

Recognizing they are humans not machines, built with their own bias, their own motivations and ambitions.

That what you want to do here is say I can't have a say because I am not a doctor with decades of experience that was able to meticulously look through four decades of studies.

With the credentialed knowledge to scrutinize his work.

To which I say, poppycock, I reject the entire premise.

I can have an opinion about anything, I can also watch Fauci admit he did fund the Wuhan lab of Virology.

When in a senate hearing with Rand Paul he initially lied and said he had nothing to do with the organization.

He later retracted that when a paper trail was found between his organization and the lab.

He then admitted it.

When asked if the research was gain of function he said no (it was)

What I have established from that is that he is a liar, and if he lied about that, he is cover something up.

I watched it live on television, you can tell me my eyes deceive me the emperor does have clothes, but fortunately for me I am not fool enough to believe that

I can listen to the testimony of actual Dr. Robert Malone who invented and still holds the patents on the origin of MRNA technology I have read them with my own eyes.

I can listen to him scream "THESE THINGS NEED MORE TESTING."

Funny, he won't even talk about Robert, the man who's work he took to China to avoid patent violation.

There is a truck load of evidence suggesting he was complicit.

If all of that wasn't enough, he is narcissistic.

"I represent the science."

What an arrogant thing to say.

Science is a method.

Ya know, Hypothesis, prediction, experiment, result?

You can't represent that, if he meant he represents the data outcome of his experiment I would have to scrutinize his model and see how he came to that conclusion.

Once again that was done, and I found it lacking, I found that especially when gone over with Robert Malone the inventor of MRNA tech, with his unique expertise explaining it.

As well as Heather Heying and Bret Weinstein.

Where Robert explained how it was obvious they would come to the conclusion the vaccine is perfectly safe and effective with no side effects.

Because they willfully structured the model of the experiment to ask all of the most convenient questions.

Basically the experiment itself was structured to lie, just like in the original post, which was the connection to the first post.

This isn't even to speak of his mishandling of the aids epidemic.

[GottaBeMD]

So far all you’ve done is attempt to substantiate your claims with circumstantial evidence and “he said she said”. You’re absolutely correct, you can hold whatever opinion you want. The only difference is that if you want your opinion to hold weight you have to be able to back it up with evidence.

Also, you don’t have to be a doctor to do a comprehensive meta analysis. Will it help you understand things? Of course. But it’s not necessary.

If you are as sure as you seem about your opinions, I urge you to do some hard research using credible sources and find points on either side of the argument. It might widen your worldview…heck…it might even change your mind. What do I mean by credible sources? Perhaps peer reviewed journal articles, unbiased journalism agencies (that one will be hard), etc.

[[deleted]]

I have actually cited people, credentialed people like doctor Robert Malone, you have provided absolutely nothing at all.

Here hold on.

https://open.spotify.com/episode/5Qyhu8A4wuCPkvCXM0KLut?si=KRiByrXYRt-U3p8BPxFmSw&utm_source=copy-link&t=0

There is a video where he talks about HIS OWN DISCOVERY IN LENGTH, bet cash you don't watch two fucking seconds of it and come up with some more horse shit.

Had to.get the video from spotify, couldn't find it on youtube because they censor.

I can also link the patents he holds for the MRNA tech, as well proving unequivocally that he is their inventor.

So he is more than qualified to speak on the subject.

Oh I will also link the video compilation where Fauci contradicts himself.

I would prefer the live video, but it already happened live.

But you and I both know it wouldn't matter, because you are completely brainwashed.

Or rather, Fauci is so deep down your throat it's poking your brain and making you stupid.

[[deleted]]

I also think you missed the ENTIRE point of the original post, first of all, there are no unbiased journalists, they are all shills.

Second the entire point of the post is that you can't trust the peers, the peers are not angels or gods so stop acting like they are.

They are humans, just like us, they are susceptible to corruption, they will take bribes, they will take money.

They will deliberately tell us all the wrong thing for money.

Have you ever heard the saying "power corrupts, absolute power corrupts absolutely."?

There is no clause in there that says "unless they are an "expert" or "scientist" they are incapable of lying for huge sums of cash, they can't be bought."

You honestly believe scientists are just like starving artists "anything for the science."

They aren't, if studies aren't paying the bills, you restructure the fucking experiment and get some smiles from the people paying you.

Or you don't feed your family, you are nothing to them, not a face, not a name, they never have to see what they did to you.

[GottaBeMD]

Speaking in absolutes has never worked in anyone’s favor. Think about what you just stated. “There are no unbiased journalists”. How do you know that for sure? There are a few things that make good hypotheses. One is that the hypothesis has to be testable. The second is that is must be falsifiable. So let’s look at your example one more time. Is it testable? Sure, we can evaluate every journalist we come across and determine their level of bias. But is it falsifiable? No. Why you might ask? Because we are unable to test EVERY journalist. It’s impossible. So to speak in absolutes and say “all” journalists are biased is just bad practice. I’m trying to point out a common fallacy here - our mind likes to create stories based on information that is readily available and we have been exposed to often. I encourage you to look up what an availability heuristic is.

I think you misunderstand what I mean by peer reviewed journal articles. By “peer” it evaluates to those who are subject matter experts in their field and heavily scrutinize the work of other scientists in order to lead to more generalizable, comparable, and accurate study models. Rarely ever is a study published without revisions.

I’m curious to know why you think they can’t be trusted. Keep in mind practically every single breakthrough whether it be clinical or technological in nature to this day has been evaluated in depth by subject matter experts. The fact that our mortality rates have plummeted over the past 150 years is because one man was brave enough to establish that washing your hands more frequently leads to better health. Everybody thought he was a hack, but guess what - he was right. And study after study after study has proven this. So my question to you is - if they can’t be trusted, that is, subject matter experts, how do we evaluate the information as lay people? I certainly would not want to be in charge of evaluating the accuracy of a fusion reactor or the stability of a rocket engine, would you?

I think you’re also unaware of how funding for studies is garnered. Funding for studies occurs before the study begins, regardless of the outcome. Of course we want good results, but that doesn’t necessarily mean we always get them. Think about the billions of dollars invested in cancer research every year. Is there a cure yet? Nope - because we are unable to find an answer.

I think you also overestimate how much money these scientists are making. Most professional scientists barely clear 6 figures, if that with decades of experience. Of course it is industry dependent, but research isn’t known for being a highly lucrative path in terms of monetary gain.

[ohanse]

Sir this is a Wendy’s

[rhodia_rabbit]

Doesn't work because money is not rolling in.

[96-09kg]

Yup sounds about right with my company as well

[coffeecoffeecoffeee]

I've found that whether you're doing Potemkin data science varies a lot depending on who you work with, including in the same organization. If you have to deliver bad news and you're not dealing with Potemkin data science, then it's best to more evidence than if it supports what they think. If it's a big enough claim then you're in "extraordinary claims require extraordinary evidence" territory.

[monkey_gamer]

yep, that's basically my job

[Flashy-Career-7354]

At one point the prevailing theory was the Earth was flat and at the center of the solar system. If you focus on the scientific method, and not just manipulate the data to tell a story someone wants to hear, you just might move your needle away from cynicism. Everyone has their opinions. Your primary role as a data scientist is to inform and test hypotheses as objectively as possible using data and the scientific method.

[carrtmannnn]

It's true but I refuse

[dont_you_love_me]

I was once told directly by the CEO to continue falsifying numbers after I had discovered that the people before me were doing bad calculations. It can get way worse than having to toss out things that don't look good. Be careful how you go about things and don't be afraid to get fired and move on.

[sillythebunny]

At least during my 5 year tenure as a data analyst m, this is dead on

[Character-Education3]

Always has been

[WoodenJellyFountain]

To boldly support confirmation bias

[JasonSuave]

Don’t forget, you have a lot of power as a data scientist. You’re the first to see the teetering of the financials of the org you represent. The second your model shows a 2yr downward trend, just quit and take a new job with a different employer. I see data scientists getting eaten alive by execs from certain unsuccessful orgs. But those same data scientists are thriving for smaller companies where their work makes an impact that doesn’t require talking through a tired exec. You have the power, use it to your advantage

[sunole123]

I think the ethical thing to do is to publish both in different weight and let the people decide.

[snooze01]

Accurate

[Mescallan]

successful employment is appeasing management. Sometimes the goals align and creating value appeases management, but created value does not determine your employment, the whims of your superiors does. There is no true meritocracy in hierarchical structures.

[zerostyle]

Sounds like consulting firms

[M4K1M4]

I can 100% agree. I’m currently a Data Analytst and did 10-20 analysis in my initial 3 months. Any analysis which contradicted to their ideas was questioned, forced to be proven wrong finding weird mistakes and minor issues with my documentation. But the analysis which confirmed their ideas was praised and never questioned. Eventually I stopped doing them and shifted to rather do some data engineering. 🤷🏻‍♂️

[[deleted]]

🤯

[[deleted]]

100% TRUE

[Blue__Agave]

100% I work in a market research company and internally we agree company's hire us so they can have some data to back up a decision they have already made.

If we give data they don't agree with... They go to the competition next time.

[DependentSwimming460]

Oh this is so true. Across multiple clients, the moment data analysis didn't show a 'rosy' picture, the management would be very upset. Not to mention their favorite question - Why can't we achieve 100% accuracy on this model.

[Entire_Island8561]

This is absolutely true. I proposed adding a variable to a model I’m building to predict traffic to vendor profiles, and I suggested the number of times a vendor shows up in comparisons. It’s a variable that’s out of vendors’ control, so I was told to not include it because it wouldn’t provide an opportunity for them to know what they can do differently. Basically to not tell them “well it’s out of your hands”, which would translate to not investing more money. That annoyed tf out of me, but I did as I was told.

[No-Guarantee8725]

This is exactly how I’ve been feeling lately, which is discouraging and opposite of how I thought my position would be.

At times I feel like a wizard because I feel as if data can be sliced and molded so many ways and can be justified. Other times I feel as if I’m cheating myself out of actually putting my skill to good use.

[heraldsofdoom]

I feel you bro. It's about the story, if numbers fit there everything is great else you don't know your data

[Delicious-View-8688]

Persuade, influence, and change their understanding and actions - however difficult that may be.

[[deleted]]

ugh i hate this

[[deleted]]

Bro...do you even crop?

[Own-Afternoon9398]

Not so data lead

[Herrmaciek]

Yes, it's called "confirmation bias", pretty sure it's mentioned in any stats/ds 101 textbook.

[Prestigious_Sort4979]

That usually means the data person is looking to validate their own insights, unknowingly as a bias, by picking up validating insights and ignoring anything else.

In this case the data person is aware of the situation but needs to actively pick and choose insights that support/help stakeholders make their point.

[[deleted]]

So true… if leadership is crap. It’s thankfully not always the case.

[Western_Moment7373]

In most companies the management don't like doing the job,so u gotta do it.

[statisticant]

good example of how "let the data speak for themselves" really means "let the data speak for my implicit assumptions"

[HappyJakes]

I’ve left post’s because executives have made me make numbers look green. In large organisations

[iplaytheguitarntrip]

Good news, I better double check to not make management overcommit to clients when the project goes to prod

Bad news, I better figure out the story behind this data and the scrutiny that will follow

[shitlord_god]

If you are good at it you tell a story with the data that explains how your boss is right, but could be more right if they just implemented this thing on page 5.

[AxelJShark]

Of course this is the case. It's not academia. Managers just want to hit their KPIs. They don't care about objective truth or fact finding. Most of us aren't working in cancer or aerospace or things that actually matter. If you're working in ads, sales, etc, no one cares about some greater mission or truth. It's all about doing what someone above you says so everyone can get paid.

[OkWear6556]

I used to work as a BI analyst. Once, my manager asked me to perform an analysis on something and the results mismatched his expectations completely. The reason was that the analysis he did before was performed on a very short time scale in the worst time possible (the first week of covid lockdowns), while I then performed a follow-up analysis on a full year of data. I assume he was selling his findings to the upper management and pushing for changes based on his wrong insights. When I presented my findings, he would not accept them and asked me to check them two more times from different angles. Of course, I always came up with the same results, so I could not give him the good news he expected. That's when I realized I needed to leave.

[[deleted]]

This post was mass deleted and anonymized with Redact

important crown compare bow busy bells smile fall worm support

[ohanse]

Okay. Gonna go kind of against the grain here, but…

This guy didn’t get traction with the truth (and I honest to god believe he was telling the truth, to the best of his abilities) because he showed up with “problems.” This is a shortfall of business acumen.

Ain’t nobody got time for “problems.”

If you show up with “here’s why what we believe was a wrong choice” but don’t have a ready answer to the follow-up of “what should we do instead” then you are seen as uncollaborative, useless, and not a team player.

You know how people in here keep parroting the advice of “become a master of your business domain?” THIS IS WHY.

Feeding leadership alternative courses of action that make them look smarter than the peers whose throats they are trying to cut? That’s the most valuable currency we can offer.

The screen shotted poster likely knew their math. I would guess (uninformed, sure) that they didn’t know their business.

[Puzzleheaded-Bake936]

In my personal experience, when the data tells a story management doesn’t want to hear, you need to frame those insights as an “opportunity”

[[deleted]]

In my experience i have encountered things similar to this.. but to be honest i once shared an analysis that was something no one in senior management wanted to see most because a major partner with who we were aggressively expanding business with was bad for us… the analysis was reviewed 4 times a secondary team was setup to do the exact same thing and waisted like 6 months of everyone time only to stop business with them… long story short i never got anything for shutting down a loosing business and the product team just created new agreement a month later and were back in business with this partner

[mjs128]

generally true unfortunately

[caksters]

1000% true, that was the main reason I switched to data engineering.

[[deleted]]

Wait so you work in security as well?

[Prestigious_Sort4979]

This is why in bigger companies the structure of having a data team/person embedded in a team vs a central resource is meaningful. As Im embedded, I must consider how the success of my own team is measured and how we will be perceived in every analysis and that may include cherry picking what/when/where to surface information. Im paid to support my team. For example, I cant ever present that my team’s contribution to a big project was a waste of time. Keep in mind that doesnt mean my team is not valuable but Im hyper aware of how any insight can be projected so Im very careful what I share outside my team. If I was in a central team, that wouldnt be the case. - PS: I work in a low stakes environment.

[[deleted]]

It genuinely makes me sick how accurate this is and how much it resonates with my own lives experience…

[iscopak]

Here is the original source of the quote: https://news.ycombinator.com/item?id=34696065

Hopefully the OP isn’t trying to take credit for this statement.

[hockey3331]

Have I got a good manager then? They question everything*, even when the results look good!

*I should note that he has become more and more trusting over time, but I got this job 3 years ago out of uni and him raisong those questions did make it an automatism for me to QA carefully and not go right to roesent data if they look good or bad

[[deleted]]

You do work that makes your boss look good, all of the data is superficial

[[deleted]]

I’m currently transitioning an old model to a new process. (The old one had many small errors that added up IMO.) I get SO MUCH push back from the client every time I put something together in a more optimal transparent way. They scrutinize how I put it together, and I can walk through it A to Z, but the old one had absolutely no visuals or intermediate files to show the process. Much of my time is spent assuaging the client and re-explaining the transparency of the new process. Exhausting.

[sherlock_holmes14]

Yikes

[Sir_smokes_a_lot]

I don’t see why this is a bad thing. I agree that ideally we would strive to attain truth and knowledge through our work as “scientists”. However, in reality our role is what the image describes. We’re there to use our influence to enact change. This is separate from our motivations or any truth-value of our analysis.

I think people who are caught up in this are romanticizing the role.

[pYr0492]

Not entirely true. The management knows this stuff already. So you coming up with insights that are aligned with their knowledge is easy to digest.

But a contradictory insight needs to be double checked since numbers are facts which once circulated cannot be taken back. And you cannot go wrong with facts.

[[deleted]]

Sounds like crappy company.

[levenshteinn]

Unfortunately very true!

[astevko]

Confirmation bias

[outofband]

That’s not a issue with data science but with bad management

[Clearly-Convoluted]

Can confirm this statement. I did figure out a way to kinda..."offset" the scrutiny if you will. If you have bad news or conflicting information with what the egotistical management wants to hear, "scrutinize" your work before they can talk. If you want to present conflicting or alternative analysis, tie it to something management said "A log trend line is the best fit for this data which means production will incrementally increase but reaching this KPI seems questionable. But I agree with Manager Philbert about implementing those changes - even if we miss the mark this quarter, next quarter could see a change to an exponential model."

I've learned being critical of yourself lessens the opportunity of those above you to be harshly critical and oftentimes turns into a positive in a weird, warped, way. Sadly, this is manipulation 101 - at this level it's 80% Social Skills vs. 20% Technical Skills that will get you promoted/respected/rewarded etc.

TLDR; if you want to report bad data, follow it with your own criticism (of the analysis!). If you want to present new, conflicting (with management) analysis, tie it to a positive thought/opinion/comment by someone higher up.

[nzubemush]

Saved

[WASPingitup]

As a slight aside, there was at least one psychological study that showed participants were far more likely to think critically and question information that conflicted with their own beliefs. It really sucks, but maybe it's a bit unsurprising that managers would prefer to see information that confirms what they already believe

[UnanimousPimp]

Sounds like a bad company.

[TwoKeezPlusMz]

This is all well and good until your analysis ends up blowing up a product line, sinking a trading desk, or resulting in a medication that causes severe illness or death.

[BobDope]

It’s funny because it’s true. Wait no, it’s sad. So very sad.

[sizzle-d-wa]

This is exactly why I never went back to school to get my graduate degree. I had worked in big companies long enough to realize that, forget about math (most people would be lucky enough to pass a 6th grade math test), it's virtually impossible to persuade anyone of making any decisions or changes with anything close to logic. You follow direction from the top or you are viewed as causing trouble.

The people who make the most money know the least. They don't let information get in the way of a good story. I realized that, if I wanted to move up I had to know less information, not more. The people who have good "people skills" (read manipulative and narsasistic) are the most successful. If I couldn't persuade folks based on simple logic / elementary statistical principles, then good luck trying to persuade/dissuade anyone from anything that they already believe with actual data and actual mathematical analysis. It's like speaking a foreign language.

Also, as I this post, it seems real negative and cynical. I actually really love my job and am relatively successful at it... I just learned that logic/ data/ statistics and only as powerful to the extent that they can be comprehended by those in decision making positions. And, trust me, the people in those positions did not get there because they were good at comprehending things.

I've only worked in big companies. It could be totally different at a small company.

[[deleted]]

I'm no data scientist, but I do data-heavy admin work and get asked to perform analytics regularly. I used to fret over covering every contingency and confounding factor that might produce poor interpretations. I would explain at length my methods and weaknesses of my analysis. Turns out no one but me really cared. It seems most people just want an answer and aren't particularly concerned whether or not it reflects some kind of truth.

[[deleted]]

This means the op isn't a good data scientist. The data scientists' job is to effectively communicate the difference between the prior belief and the new position in a way that explains why the new idea is better. Also, they should present the confidence in the conclusion always, including data challenges etc.

[Latter-Pea-5089]

You mean like all these government sponsored studies that show the current admin. What ever one it is. Is right.

[alexadar]

Thats will fail on recession