You are currently browsing the tag archive for the ‘Bias’ tag.

We’ve done the setup.

First, the feeling: something shifts, guidance changes, and people aren’t sure what they’re looking at. Then the distinction: science has social layers around it, but the core activity—testing models against reality—is constrained by something that isn’t.

Now we get to the part that matters:

What does it look like when that constraint holds—and when it doesn’t?

Because this isn’t theoretical.

It happens in the wild.


The Simple Test People Already Use

Most people don’t talk about models or epistemology.

They do something simpler.

They watch outcomes—whether predictions land, whether explanations hold up, whether the goalposts move after the fact.

When those answers line up, trust builds, even if the conclusion is inconvenient.

When they don’t, something else starts to creep in. Not always a conspiracy. Not always bad faith. But something other than clean model-testing.


What Healthy Science Looks Like

You don’t need a textbook definition. You can recognize it by behavior.

Healthy scientific practice tends to show a few patterns. Claims are tied to specific predictions. Uncertainty is stated, not buried. Errors get corrected without theatrical reversals. Competing models are allowed to fail on their own terms.

It can still be messy. It can still be wrong.

But the direction is clear: toward tighter alignment with reality.


When It Starts to Drift

When social pressures start bending the process, the signals change.

You begin to see claims framed as conclusions first, reasoning second. Heavy reliance on consensus language instead of model performance. Criticism treated as disloyalty rather than error-checking. Revisions framed as narrative continuity instead of correction.

None of these prove corruption on their own.

But together, they form a pattern—and people pick up on that pattern, even if they can’t articulate it.


The Substitution Problem

At the core, something gets swapped.

Instead of:

Does the model work?

You get:

Does the claim align with the current consensus?

That substitution is subtle. It doesn’t announce itself. It shows up in language, in incentives, in what gets amplified and what gets ignored.

And once it happens, the whole system starts to feel different.


Why It Feels Off

People aren’t just tracking claims. They’re tracking consistency—between what was said, what actually happened, and how the update was explained.

When those line up, even large changes feel legitimate.

When they don’t, even small adjustments feel like manipulation.

That’s the gap.


Where Constructivism Gets Its Grip

When people see that gap—shifting language, inconsistent framing, institutional defensiveness—they start looking for explanations.

One of those explanations is:

Maybe truth itself is being negotiated.

That’s where strong social constructivism starts to feel persuasive.

Not because it’s correct.

Because it seems to explain what people are seeing.


The Problem With That Conclusion

It overcorrects.

It takes real failures—communication breakdowns, incentive distortions, institutional bias—and treats them as proof that scientific truth itself is socially constructed.

But those same failures tend to degrade science’s ability to do the one thing that matters:

Track reality.

Bad models don’t suddenly start working because they’re socially supported.

They fail more obviously.


The Constraint Doesn’t Go Away

Even in distorted environments, the underlying constraint is still there.

Predictions still miss. Explanations still break. Reality still refuses to cooperate.

That’s why bad theories eventually collapse, better ones replace them, and the process—however uneven—keeps moving.

Not because institutions are perfect.

Because the world doesn’t bend.


What Actually Makes This Work

If you had to compress what keeps science from collapsing into pure consensus, it isn’t a slogan or an institution.

It’s a set of recurring demands placed on any model that wants to survive.

A model has to say what happens next—and then be judged against it. It has to explain more than its competitors without multiplying assumptions. It has to hold together internally when pushed, not unravel into contradiction. And when reality doesn’t cooperate, it has to adjust rather than dig in.

None of that depends on who proposes the model. None of it depends on which institution backs it.

Those pressures come from the outside.

And they’re what make it very difficult—though not impossible—for social forces to fully take over.


What This Means for the Rest of Us

You don’t need to become a scientist to navigate this.

But you do need a clearer lens.

When you’re looking at a scientific claim, the question isn’t:

Who agrees with this?

It’s:

What would count as this being wrong—and did that test happen?

That’s the difference between evaluating a model and deferring to a position.


The Line That Still Holds

Science is done by human beings. It sits inside institutions. It’s shaped by incentives.

None of that is in dispute.

But the reason it works—the reason it produces anything usable at all—is that it runs up against something that doesn’t care about any of that.

Reality pushes back.

It doesn’t negotiate. It doesn’t care about consensus. It doesn’t adjust to save face.

And that’s the only reason the entire enterprise holds together.


Final Position

Scientific objectivity doesn’t mean:

  • scientists are unbiased
  • institutions are clean
  • conclusions never change

It means something narrower—and more important.

It means that, at its best, the process is constrained by whether its models survive contact with the world.

Everything else sits on top of that.

Sometimes cleanly.

Sometimes not.

But if you lose that constraint, you don’t just get flawed science.

You get something else entirely.

 

Further Reading

Core Philosophy of Science (Accessible but Serious)


Critiques of Strong Social Constructivism


How Science Fails (and Self-Corrects)


Modern Trust & Institutional Context

 

In the last post, we left a question hanging:

When scientific claims change, are we getting closer to reality—or just watching consensus shift?

That question only holds together if we blur two different things into one.


The Quiet Category Error

When people say “science is a social construct,” they usually point to things that are obviously true.

Research is funded by institutions. Papers move through journals. Experts decide what gets published. Language shapes how ideas are framed.

All of that is real.

None of it defines the core activity.

The mistake is simple: treating the systems around science as if they determine what makes a scientific claim true.


What Actually Gets Tested

Strip everything else away and science becomes something much more basic.

People build models of the world. Then they test them.

Not by agreement. Not by status.

By what happens when those models meet reality.

The ones that survive tend to do a few things well. They predict outcomes with some reliability. They explain more than their competitors. They hold together internally. And when new data arrives, they adjust without collapsing.

You don’t need to formalize those criteria to see them in action. You see them every time an idea quietly disappears because it stops working.

That disappearance isn’t negotiated.

It’s forced.


Influence Isn’t Determination

At this point the pushback comes quickly.

“Of course science is shaped by social forces.”

It is.

Those forces shape which questions get asked, which projects get funded, how results are presented, and how quickly findings spread. They can slow progress. They can distort it. Sometimes they can derail it for a while.

But they don’t determine whether a model tracks reality.

That’s the line.

You can delay discovery. You can confuse it. You can wrap it in bad language.

You can’t make a false model reliably predict outcomes just by agreeing that it does.


The Strong Claim—and the Weaker One

There’s a distinction that tends to get skipped.

A weaker claim says: science is socially embedded. That’s true and not especially controversial.

A stronger claim says: scientific truth itself is negotiated—shaped by power, language, and consensus.

That’s the one doing the real work.

And it doesn’t hold up under pressure.

If truth were negotiated, models wouldn’t behave the way they do across different contexts. They wouldn’t converge. They wouldn’t travel.


Pluto Didn’t Change

Take the familiar example of Pluto.

At one point, there were nine planets. Now there are eight.

On the surface, that looks like a shifting fact.

But nothing about Pluto changed. What changed was the classification system, refined in response to better observations and clearer criteria.

Run that process again—with the same data, in different countries, under different institutions—and you land in the same place.

That’s not consensus creating truth.

That’s constraint forcing alignment.


Authority Doesn’t Carry the Argument

Another social constructivist argument leans on expertise.

Science is what recognized experts agree on. Experts are socially validated. Therefore science is socially constructed.

Except that’s not how progress behaves.

Clyde Tombaugh discovered Pluto without formal credentials.

Michael Faraday made foundational contributions to electromagnetism with little formal training.

Their work wasn’t accepted because of status.

It was accepted because it held up.

Credentials can signal competence. They don’t determine whether a model survives contact with the world.


When Bias Enters, Science Starts to Fail

The strongest objection points to real failures.

“What about biased or harmful science?”

Those cases matter.

But look closely at what they show.

Take the Tuskegee syphilis study. It wasn’t just unethical. It was methodologically broken—biased sampling, invalid comparisons, contaminated conditions.

The result wasn’t just immoral.

It was useless as knowledge.

The same pattern appears elsewhere. Once ideology starts steering the model, predictive accuracy drops. Explanations weaken. The work stops holding together.

That isn’t science revealing its true nature.

That’s science breaking down.


A Brief Note on Paradigm Shifts

You’ll sometimes hear this framed in terms of paradigm shifts.

“If scientific frameworks change, doesn’t that mean knowledge is constructed?”

Frameworks do shape how data gets interpreted.

They don’t rescue models that fail.

When predictions stop landing and explanations start stretching, the model gives way.

Not because consensus changed.

Because it stopped working.


The Outer Layers Still Matter

None of this denies the obvious.

Funding is political. Publication standards are negotiated. Ethics are socially enforced.

These shape the environment science operates in. They can slow it down. They can distort it. They can even temporarily misdirect it.

But they don’t decide what’s true.

Because truth, in this context, isn’t assigned.

It’s encountered out there in the wild.


Back to the Tension

So when scientific claims change, what are we seeing?

Sometimes it’s better data refining a model. Sometimes it’s uncertainty narrowing over time.

Sometimes it’s institutional incentives shaping how results are framed.

The two get mixed together.

That’s why it feels unstable.

But only one of those layers determines whether the model actually works.


The Constraint That Holds

You can treat science as just another narrative shaped by power.

If you do, its authority collapses into politics.

Or you can recognize the constraint:

Reality pushes back.

It pushes back the same way regardless of who’s asking the question, what language they use, or which institution is involved.

That doesn’t make science perfect.

It makes it bounded.

And that boundary is the reason it works at all.


Where That Leaves Us

Science changes.
Scientists are biased.
Institutions are political.

None of that makes the core activity a social construct.

Because the core isn’t built out of agreement.

It’s built out of whether the model survives contact with the world and makes no distinction of who you are.

 


Glossary

Social Construct
An idea or category whose defining features depend on social agreement and can vary across cultures (e.g., money, legal systems).

Scientific Model
A structured representation used to explain and predict phenomena.

Empirical Constraint
The requirement that a model must align with observable reality.

Predictive Accuracy
How reliably a model forecasts outcomes.

Explanatory Power
How well a model accounts for observed phenomena relative to alternatives.

Coherence
Internal consistency within a model.

Model Robustness
The ability to adapt to new data without collapsing.

References

Something feels off. You can hear it in the way certain arguments move too quickly, collapsing a complex moral landscape into a stark choice. On one side, morality is said to be subjective—nothing more than preference, culture, or perspective. On the other, we are told that without objective grounding, morality collapses into power. The argument is clean, decisive, and rhetorically effective. It is also incomplete.

The appeal of this framing lies in its speed. If morality is subjective, then moral claims reduce to preference. If they reduce to preference, there is no truth to adjudicate between them. And if there is no truth, disagreement can only be resolved through assertion and enforcement. The conclusion follows with a kind of mechanical certainty: without objective morality, ethics becomes power. It is a compelling chain, particularly in live discussion, where the pressure to respond quickly prevents careful unpacking. But the speed of the move is part of its strength—and its limitation. It skips over something most people already rely on in practice, even if they do not articulate it.

In everyday life, we do not treat all moral claims as interchangeable. Some feel as though they hold even in the face of disagreement; others do not. What distinguishes them is rarely stated explicitly, but it shows up in how people respond to rules and expectations. A simple test often operates in the background: does the rule apply both ways? Does it still make sense when the roles are reversed? Does it remain defensible when you are no longer the one benefiting from it?

You can see this play out in familiar disputes. A rule that restricts speech when it targets your side may feel justified; the same rule, applied in reverse, often feels like suppression. A policy that advantages your group can look like fairness in one direction and bias in the other. The reaction people have in those moments—that sense that something has shifted or isn’t being applied evenly—is not random. It’s the symmetry test quietly asserting itself.

“The question isn’t whether a rule benefits you—it’s whether it still makes sense if it doesn’t.”

When the answers line up, the rule tends to feel legitimate. When they don’t, something begins to grate. This is not a formal proof of moral truth. It is, however, a constraint on what people are willing to accept.

One way to bring that constraint into focus is through the thought experiment proposed by John Rawls. Imagine choosing the rules of a society without knowing who you will be within it—your position, your advantages, your vulnerabilities. From that standpoint, you cannot design the system to suit your own interests. You are forced to consider whether the rules would still be acceptable if you ended up on the losing side of them. Rawls does not claim to discover moral truth through this device. What he does is remove the most obvious avenue for bias and ask what remains once that advantage is gone.

What remains is not a set of metaphysical truths written into the structure of the universe. It is something more modest and, in practice, more useful: a constraint on justification. Some rules cannot be defended once you no longer know where you will stand. They rely too heavily on asymmetry, on the assumption that the person invoking them will not have to bear their cost. When that assumption is removed, the rule loses its force. This does not make morality objective in the way physical laws are objective, but it does show that not all moral systems are equally defensible.

This is the space the binary argument overlooks. Morality does not have to be either fully objective in a metaphysical sense or entirely subjective and arbitrary. Most functioning moral systems occupy a middle ground. They are constructed and maintained through norms, institutions, and shared expectations, but they are also bounded by the conditions under which human beings live. We are vulnerable, dependent, and engaged in repeated interaction. Rules that exploit these conditions too aggressively tend to collapse under their own weight. Rules that can survive role reversal and long-term interaction tend to persist. They are not inevitable, but neither are they arbitrary.

The force of the “collapse into power” argument comes from its focus on weak forms of subjectivism. If morality is reduced to mere preference, then the conclusion follows quickly. But this is not how most moral reasoning operates in practice. Even absent a claim to objective truth, people appeal to considerations that go beyond preference: reciprocity, fairness, stability, and the costs of defection. These are not metaphysical foundations, but they are not empty either. They generate real limits on behavior and real expectations about what can be justified.

The question, then, is not simply whether morality is objective. That framing compresses too much into a single term. A more useful question is what constrains moral reasoning so that it does not collapse into preference or power. Rawls offers one answer in the form of symmetry under uncertainty. Ordinary social life offers another in the form of rules that must hold under repetition and reversal. Both point to the same underlying fact: moral systems are not free to take any shape whatsoever. They are limited by the requirements of justification and the conditions of human interaction.

This brings us back to the original feeling that something is off. That reaction often arises when a rule is applied inconsistently, when a principle shifts depending on who benefits, or when an argument demands compliance without offering a justification that would hold if positions were reversed. You do not need a fully developed moral philosophy to recognize that pattern. You only need to notice when the symmetry breaks.

Scientific objectivity does not require perfect scientists; it requires that their models survive contact with reality. Moral objectivity, if the term is to mean anything useful, does not require metaphysical certainty. It requires that the rules we live by survive contact with each other—across differences in position, power, and perspective. That is a narrower claim than the one often made in debate, but it is also a more defensible one.

Morality does not need to be written into the fabric of the universe to resist collapse. It needs something simpler: rules that can be justified without knowing who will bear their consequences, and that continue to function when they are applied to anyone over time. Once that is clear, the stark choice between objective truth and raw power begins to lose its grip. The problem is not that morality lacks a foundation, but that we often look for it in the wrong place.


Where This Goes Next

The question raised in the previous discussion—whether anything can meaningfully constrain our claims without collapsing into preference or power—does not end with morality.

It appears again, more sharply, in how we think about science itself.

If there is no constraint beyond social agreement, then scientific claims begin to look like moral ones at their weakest: negotiated, enforced, and revised under pressure. If there is a constraint, then we need to be precise about what it is and how it operates, because that distinction determines whether we are tracking reality or simply tracking consensus.

The essays that follow take up that question directly. They move from the same starting point—something feels off—to a clearer account of what, if anything, resists that collapse.

“Trump Derangement Syndrome” (TDS) isn’t a medical condition. It’s a rhetorical label for a recognizable pattern: Donald Trump becomes the organizing centre of political perception, so that every event is interpreted through him, and every interpretation is pulled toward maximal moral heat. Even people who agree on the facts can’t agree on the temperature, because the temperature is the point. Psychology writers describe it as a derogatory term for toxic, disproportionate reactions to Trump’s statements and actions.

And when politicians try to literalize it as a clinical diagnosis, it collapses into farce. It is fundamentally a political phenomenon, not a psychiatric one.

The useful question isn’t “Is Trump uniquely bad?” Reasonable people can say yes on qualities character, norms, rhetoric, policy, whatever. The useful question is: when does valid criticism become TDS? The answer is: when Trump stops being an object of analysis and becomes a gravity well.

What TDS looks like (beyond normal criticism)

Normal criticism is specific: this policy, this consequence, this evidence, this alternative. TDS is different in kind.

  • Totalization: Trump isn’t a president with a platform; he’s a single-cause explanation for everything.

  • Asymmetry: Similar behaviour in other leaders is background noise; in Trump it becomes existential threat (or, on the other side, heroic 4D chess).

  • Incentive blindness: The critic’s emotional reward (“I signaled correctly”) overrides the duty to be precise.

  • Predictable misreads: Even when Trump does something ordinary or mixed, it must be either apocalypse or genius.

This is why the term persists. It points generallyat a real cognitive trap: a personality-driven politics that makes judgment brittle. (It also gets used cynically to dismiss legitimate criticism; that’s part of the ecosystem, too.)

Why Canadian media amplifies it

Canada didn’t invent Trump fixation. But Canadian legacy media has strong reasons to keep Trump on the homepage. The reasons, in question, are not purely ideological.

1) Material proximity (it’s not “foreign news” in Canada).
When the U.S. president threatens tariffs, trade reprisals, or bilateral negotiations, Canadians feel it directly: jobs, prices, investment, and national policy all move. In Trump’s second term, Canadian economic and political life has repeatedly been forced to react to U.S. pressure: tariffs, trade disputes, and negotiations that shape Ottawa’s choices.

That creates a built-in news logic: Trump coverage is “domestic-adjacent,” not optional.

2) An attention model that rewards moral theatre.
Trump is an outrage engine. Outrage is a business model. Canadian mediais operating in a trust-and-revenue squeeze, and that squeeze selects for stories that reliably produce engagement. Commentators on Canada’s media crisis have argued that the Trump era intensified the trust spiral and the incentives toward heightened, adversarial framing.

3) Narrative convenience: Trump as a single, portable explanation.
Complex stories (housing, health systems, provincial-federal dysfunction) are hard. Trump is easy: one villain (or saviour), one emotional script, one endless drip of “breaking.” This is where amplification turns into distortion. A real cross-border policy dispute becomes a morality play; a complicated negotiation becomes a personality drama.

4) Coverage volume becomes self-justifying.
Once a newsroom commits, it has to keep feeding the lane it created. Tools that track Canadian legacy-media coverage of Trump-related economic conflict like tariffs for example, show how sustained and multi-outlet that attention can become.

The more space Trump occupies, the more “newsworthy” he becomes, because “everyone is talking about it” (including the newsroom).

None of this requires a conspiracy. It’s mostly incentive alignment: relevance + engagement + a simple narrative hook.

The cost: Canadians inherit America’s temperature

The predictable result is that Canadians import not just U.S. events, but U.S. emotional calibration.

  • Canadian politics gets interpreted as a shadow-play of American factions.

  • Domestic accountability weakens (“our problems are downstream of Trump / anti-Trump”).

  • Readers get trained to react first and think second, a reinforcing heuristic, because that’s what the coverage rewards.

And it corrodes trust: if audiences can feel when coverage is performing emotional certainty rather than reporting reality, they stop believing the institution is trying to be fair.

A reader’s heuristic: the TDS check

If this is going to be useful (not tribal), it needs a diagnostic you can run on yourself and on coverage:

  1. Specificity test: Is the criticism about a policy and its consequences, or about Trump as a symbol?

  2. Symmetry test: Would you report/feel the same way if a different president did it?

  3. Proportionality test: Does the language match the evidence, or does it leap straight to existential claims?

  4. Update test: When new facts arrive, does the story change—or does the narrative stay fixed?

  5. Trade-off test: Are costs and alternatives discussed, or is “opposition” treated as sufficient analysis?

Pass those tests and you’re probably doing real criticism. Fail them repeatedly and you’re in the gravity well regardless of whether the content is rage or adoration.

The verdict

Trump is a legitimate target for strong criticism especially in a second term with direct consequences for Canada.

But the deeper media failure is not “being anti-Trump.” It’s outsourcing judgment to a narrative reflex: a system that selects for maximal heat, maximal frequency, and minimal precision. That’s how valid critique curdles into derangement—because it stops being about what happened, and becomes about what the story needs.

The fix is boring, which is why it’s rare: lower the temperature, raise the specificity, and let facts earn the conclusion.

Psychology Today — “The Paradox of ‘Trump Derangement Syndrome’” (Sep 5, 2024)
https://www.psychologytoday.com/ca/blog/the-meaningful-life/202409/the-paradox-of-trump-derangement-syndrome

The Loop (ECPR) — “Is ‘Trump Derangement Syndrome’ a genuine mental illness?” (Oct 13, 2025)

Is ‘Trump Derangement Syndrome’ a genuine mental illness?

CBS News Minnesota — “Minnesota Senate Republicans’ bill to define ‘Trump derangement syndrome’ as mental illness…” (Mar 17, 2025)
https://www.cbsnews.com/minnesota/news/trump-derangement-syndrome-minnesota-senate-republicans/

Reuters Institute — Digital News Report 2025: Canada (Jun 17, 2025)
https://reutersinstitute.politics.ox.ac.uk/digital-news-report/2025/canada

The Trust Spiral (Tara Henley) — The state of media/trust dynamics (May 2024)

The Trust Spiral

Reuters — “Trump puts 35% tariff on Canada…” (Jul 11, 2025)
https://www.reuters.com/world/us/trump-puts-35-tariff-canada-eyes-15-20-tariffs-others-2025-07-11/

Financial Times — “Canada scraps tech tax to advance trade talks with Donald Trump” (Jun 30, 2025)
https://www.ft.com/content/4cf98ada-7164-415d-95df-43609384a0e2

The Guardian — “White House says Canadian PM ‘caved’ to Trump demand to scrap tech tax” (Jun 30, 2025)
https://www.theguardian.com/world/2025/jun/30/canada-digital-services-tax-technology-giants-us-trade-talks

The Plakhov Group — Trade War: interactive visualizations of Canadian legacy-media coverage of Trump’s tariffs (Feb–Sep 2025 dataset)
https://www.theplakhovgroup.ca/detailed-briefs/trade-war-interactive-visualizations

The fascinating bit here is how easy it is for us to fool ourselves into thinking we’re doing “x”, when in reality we are doing “y”. In this study, all that was required to mirror the bias in our society against women was for a company to have a policy of meritocracy in place. Under the aegis of this policy people in the study tuned out their thoughts and considerations for actual fairness and stopped appraising their actions.

      “When it came time to divvy up $1,000 in bonus money, there was a stark divide between participants in the meritocracy and non-meritocracy conditions. When the fictional company stressed fairness and individual performance, subjects gave men about 12 percent more than equally qualified women on average. When it didn’t mention a focus on merit, there was no significant difference between the bonus for men and women.

     Though the experiment didn’t provide specific insights into the reasons for the different results, based on previous academic work, Castilla and Benard suggest that the variance might have to do with the participants’ confidence in their own judgement. In agreeing with the company’s meritocratic principles, they might have bolstered their sense of their own objectivity or felt they had established their “moral credentials” as non-prejudiced people.

     “An organizational culture that prides itself on meritocracy may encourage bias by convincing managers that they themselves are unbiased, which in turn may discourage them from closely examining their own behaviors for signs of prejudice,” Castilla and Benard write.”

And there be the one of the problems with existing within a society that has normalized patriarchal standards.  It is so very easy to forget that the very societal air we breathe comes with a implicit set of normative attitudes that, when not consciously opposed, take over.  This is why not conforming to patriarchal expectations is tiring because feminists know that the ‘autopilot’ is complete trash and must always be on manual control.

[Source:JSTOR]

Consider the Hollywood actor giving the classic “follow your dreams and never give up” line is bad advice and is pure survivorship bias at work.  Well what is surviorship bias?  Let’s take a look friends and learn. :)

 

“Survivorship bias, or survival bias, is the logical error of concentrating on the people or things that “survived” some process and inadvertently overlooking those that did not because of their lack of visibility. This can lead to false conclusions in several different ways. The survivors may be actual people, as in a medical study, or could be companies or research subjects or applicants for a job, or anything that must make it past some selection process to be considered further.

Survivorship bias can lead to overly optimistic beliefs because failures are ignored, such as when companies that no longer exist are excluded from analyses of financial performance. It can also lead to the false belief that the successes in a group have some special property, rather than just coincidence (Correlation proves Causation). For example, if three of the five students with the best college grades went to the same high school, that can lead one to believe that the high school must offer an excellent education. This could be true, but the question cannot be answered without looking at the grades of all the other students from that high school, not just the ones who “survived” the top-five selection process.

Survivorship bias is a type of selection bias.”

 

“During World War II, the statistician Abraham Wald took survivorship bias into his calculations when considering how to minimize bomber losses to enemy fire. Researchers from the Center for Naval Analyses had conducted a study of the damage done to aircraft that had returned from missions, and had recommended that armor be added to the areas that showed the most damage. Wald noted that the study only considered the aircraft that had survived their missions—the bombers that had been shot down were not present for the damage assessment. The holes in the returning aircraft, then, represented areas where a bomber could take damage and still return home safely. Wald proposed that the Navy instead reinforce the areas where the returning aircraft were unscathed, since those were the areas that, if hit, would cause the plane to be lost.[8][9]”

 

 

So, they said: the red dots are where bombers are most likely to be hit, so put some more armor on those parts to make the bombers more resilient. That looked like a logical conclusion, until Abraham Wald – a mathematician – started asking questions:

– how did you obtain that data?
– well, we looked at every bomber returning from a raid, marked the damages on the airframe on a sheet and collected the sheets from all allied air bases over months. What you see is the result of hundreds of those sheets.
– and your conclusion?
– well, the red dots are where the bombers were hit. So let’s enforce those parts because they are most exposed to enemy fire.
– no. the red dots are where a bomber can take a hit and return. The bombers that took a hit to the ailerons, the engines or the cockpit never made it home. That’s why they are absent in your data. The blank spots are exactly where you have to enforce the airframe, so those bombers can return.

This is survivorship bias. You only see a subset of the outcomes. The ones that made it far enough to be visible. Look out for absence of data. Sometimes they tell a story of their own.

BTW: You can see the result of this research today. This is the exact reason the A-10 has the pilot sitting in a titanium armor bathtub and has it’s engines placed high and shielded.

 

If you want to think scientifically, ALWAYS ask what data was included in a conclusion. And ALWAYS ask what data was EXCLUDED when making a conclusion.

[Source:dieselpunksnotdead]

Americans force fed their concerns from the elites handbook need to see Liberal Viewers 101 guide to the economy or even just to get a sense of what is going wrong and how crazy it is to support people who are for “more of the same”.

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Unpolished XX

No product, no face paint. I am enough.

Volunteer petunia

Observations and analysis on survival, love and struggle

femlab

the feminist exhibition space at the university of alberta

Raising Orlando

About gender, identity, parenting and containing multitudes

The Feminist Kitanu

Spreading the dangerous disease of radical feminism

trionascully.com

Not Afraid Of Virginia Woolf

Double Plus Good

The Evolution Will Not BeTelevised

la scapigliata

writer, doctor, wearer of many hats

Teach The Change

Teaching Artist/ Progressive Educator

Female Personhood

Identifying as female since the dawn of time.

Not The News in Briefs

A blog by Helen Saxby

SOLIDARITY WITH HELEN STEEL

A blog in support of Helen Steel

thenationalsentinel.wordpress.com/

Where media credibility has been reborn.

BigBooButch

Memoirs of a Butch Lesbian

RadFemSpiraling

Radical Feminism Discourse

a sledge and crowbar

deconstructing identity and culture

The Radical Pen

Fighting For Female Liberation from Patriarchy

Emma

Politics, things that make you think, and recreational breaks

Easilyriled's Blog

cranky. joyful. radical. funny. feminist.

Nordic Model Now!

Movement for the Abolition of Prostitution

The WordPress C(h)ronicle

These are the best links shared by people working with WordPress

HANDS ACROSS THE AISLE

Gender is the Problem, Not the Solution

fmnst

Peak Trans and other feminist topics

There Are So Many Things Wrong With This

if you don't like the news, make some of your own

Gentle Curiosity

Musing over important things. More questions than answers.

violetwisp

short commentaries, pretty pictures and strong opinions

Revive the Second Wave

gender-critical sex-negative intersectional radical feminism