Natural Sciences

Perspectives

Sean Coleman

2026-05-07

Perspectives and Objectivity

Science is supposed to be objective…

  • No perspectives — just the world as it is
  • No bias — just data and logic
  • No politics — just knowledge

“Science tells us what is actually out there in the world — there seems to be no space for different perspectives.”

Today we test that claim.

Today’s Questions

  1. Are there perspectives in natural science — and is that a problem?
  2. How reliable are scientific observations?
  3. Who gets to do science — and does it matter?
  4. What are the deepest challenges to scientific knowledge?

Perspectives in Science

Are There Perspectives in Science?

Yes — in almost every field:

  • Physics: competing interpretations of dark matter, dark energy, string theory, quantum gravity
  • Biology: all biologists accept evolution — but what does evolution mean?
    • Modern Synthesis: natural selection on random genetic mutation
    • Modern extensions: environmental/epigenetic factors switch genes on or off
  • Chemistry: even at elementary levels — what is really happening in a redox reaction?

Science is not perspective-free. The question is: is that a problem?

Two Views: Unity vs. Pluralism

Unity of Science

One world → one true theory

Multiple perspectives = unfinished business

We haven’t found the truth yet.

Fits the notebook metaphor

Pluralism

Many maps of one territory

Different theories serve different purposes

A patchwork quilt is fine — and more honest about how science actually works.

Fits the map metaphor

Do you feel uncomfortable with perspectives in science? You may yourself have a perspective: the unity of science perspective.

Case Study: Is Light a Wave or a Particle?

  • 17th C: Huygens — wave theory. Explains diffraction and interference.
  • 18th C: Newton — particle theory. Explains reflection and refraction. Cannot explain diffraction.
  • Early 19th C: Fresnel — improved wave theory; explains diffraction mathematically.
  • 1905: Einstein — light is quantised into photons (explains photoelectric effect).
  • Result: wave-particle duality — light behaves as both, depending on the experiment.

Is this two perspectives — or one complex perspective with two aspects?

Popper and Falsificationism

Karl Popper (1902–1994): the job of science is to falsify hypotheses, not confirm them.

The black swan problem:

No matter how many white swans you have seen, one black swan proves “all swans are white” is wrong.

We can never prove a universal statement — but we can disprove it.

Therefore: science should try to prove hypotheses wrong.

H₀ (null hypothesis) No relationship exists between variables
H₁ (experimental hypothesis) A relationship exists

Scientists falsify H₀ — they don’t try to confirm H₁.

Three Problems with Popper

1. Self-refutation: Popper’s falsifiability principle is itself not falsifiable. By its own standards, it is meaningless.

2. The Duhem-Quine thesis (auxiliary hypotheses): You can’t test a hypothesis alone. Every experiment also tests the equipment, the theory behind the equipment, and dozens of other assumptions. If the result is wrong → maybe the hypothesis is wrong. Or maybe the equipment is broken. “The equipment didn’t work” — a classic way to protect a hypothesis.

3. Stubborn theorists: Popper thought scientists should drop theories in the face of contrary evidence. But they don’t — and shouldn’t. Continental drift theory: evidence was overwhelming by 1905 → accepted only in 1960.

The Periodic Table: Sticking to Theory

When noble gases were discovered in the 1890s, they didn’t fit Mendeleev’s table.

Did scientists reject the periodic table?

No. They worked on it and added a new column six years later.

In 1913, Henry Moseley solved the remaining anomalies by ordering elements by atomic number rather than atomic weight.

Anomalies don’t immediately overthrow theories. They drive science forward — and that is what makes it human.

Discussion Question 1 (pairs · 90 sec)

“Popper says scientists should immediately abandon a hypothesis when evidence falsifies it.

Is this good advice? What would be lost if scientists always followed it?”

The Challenges of Observation

Science Is Messier Than the Textbook Says

Scientific knowledge is a hodge-podge:

  • Different kinds of reasoning in different situations
  • Different models work in different contexts
  • Material tools with their own quirks and practical fudges

“Let’s face it — scientific knowledge is messy. And this messiness is challenging. And that is what makes it interesting — and human.”

Three layers of challenge: observations, testing hypotheses, and deeper methodology.

Practical Challenges with Equipment

  1. Skill and dexterity: instruments require know-how (maze knowledge) beyond theoretical knowledge.
  2. Reading measurements: accurately, without bias from expectations.
  3. Equipment malfunction: even the most sophisticated experiments go wrong.

OPERA experiment, 2011 (CERN): Neutrinos sent from CERN to Gran Sasso appeared to travel faster than light — physically impossible.

Cause: a faulty fibre-optic cable.

Even a billion-euro particle physics experiment is not immune to a loose connection.

Technology Can Mislead

fMRI brain imaging:

  • Images are statistical compilations of hundreds of pictures — but appear as single images
  • Artificial colour schemes make small variations appear as large changes in hue
  • Typical brain activity changes are distributed across larger areas than images suggest
  • Risk: researchers conclude results are more definitive than they really are

Just because something is technologically possible doesn’t mean it’s the right investigation.

Technology can lead the science rather than serve it.

Observation Is Never Neutral

Two deeper problems:

1. Selectivity: We always observe something specific, guided by theory. But to know which variables to observe, we need to know which are relevant — which requires knowing the answer we’re looking for. A circular situation.

2. Theory-laden observation (“seeing-as”): All observations are structured by prior concepts.

“There is no such thing as immaculate perception.” — Nietzsche

  • We cannot see mass without the concept of mass.
  • We cannot see that a plant is flowering without the concept of a life cycle.
  • A cat can see the light from a laptop — but cannot see it as a computer.

Case Study: Martian Canals

  • 1877: Italian astronomer Giovanni Schiaparelli observes /canali/ (channels) on Mars — natural geological features.
  • Mistranslation: /canali/ → ‘canals’ in English → implies artificial structures.
  • Percival Lowell (US): publishes a 400-page book (1906) mapping an elaborate network of Martian canals — built by an advanced civilisation to transport water from the poles.
  • Widespread belief persists for several decades.
  • Resolution: more powerful telescopes and space missions reveal the ‘canals’ were strings of craters in linear formations.

One mistranslated word → decades of distorted observations by trained astronomers.

Expectations are powerful enough to shape what expert scientists actually see.

Confirmation Bias and the N-Ray Scandal

Confirmation bias: tendency to notice and emphasise evidence that confirms a hypothesis, and downplay evidence that disconfirms it.

René Blondlot (1903, France): claimed to discover a new form of radiation — N-rays — as differences in spark brightness.

Others tried to replicate → failed.

Professor Robert Wood (sent by /Nature/ to investigate): - Secretly removed an aluminium prism central to the experiment - Blondlot continued to report seeing the N-ray effects

Blondlot’s observations were a product of confirmation bias and expectation — not a real physical phenomenon.

Discussion Question 2 (pairs · 90 sec)

“If all observation is theory-laden and subject to confirmation bias, does this undermine the objectivity of science?

Or does the process of peer review and replication rescue it?”

Who Does Science?

The Socio-Cultural Perspective

Given that science is a social activity, different social groups produce different perspectives.

Key question: Who has historically been allowed to participate — and what knowledge might we have missed?

In the 17th-century Scientific Revolution and the Enlightenment, science was overwhelmingly conducted by:

  • Men
  • Usually aristocratic men

Not because women had nothing to contribute — but because they were systematically excluded.

The Three Barriers

Women were excluded from science through:

  • Denial of education — higher education was not available to women
  • Denial of institutional membership — scientific societies excluded women
  • Exclusion from publication — women’s work was not accepted in scientific journals

The stereotype threat: the long-standing narrative that girls and women are not as good at science as men.

This narration is now changing — but the structural barriers took centuries to dismantle and their effects persist.

Women Who Made It Anyway

Ada Lovelace (1815–1852)

First computer programmer.

Designed an algorithm for Babbage’s Analytical Engine — a computer not yet built.

Foresaw computers used for letters, everyday tasks — 100 years before it happened.

The language ADA is named after her.

Marie Skłodowska-Curie (1867–1934)

Only person to win Nobel Prizes in two different sciences (Physics 1903; Chemistry 1911).

Initially omitted from the 1903 Nobel nomination.

First woman to teach at the Sorbonne.

Her daughter Irène also won a Nobel Prize.

More Recent Figures

Mae C. Jemison (b. 1956)

First African-American woman in space (1992).

Chemical engineering degree (Stanford); medical degree (Cornell).

Served with Peace Corps before joining NASA.

Founded The Jemison Group; professor at Dartmouth and Cornell.

Katie Bouman (b. 1989)

Computer scientist.

Her algorithm produced the first ever image of a supermassive black hole (galaxy M87, 2019).

2020 Nobel Prize in Chemistry: Emmanuelle Charpentier & Jennifer Doudna — CRISPR-Cas9.

The Representation Problem

It is not just women. Consistently underrepresented in STEM:

  • People with disabilities
  • Ethnic minorities
  • People from low socio-economic backgrounds

Most major research centres: Europe, US, China, Japan.

What is the role of political power in the pursuit of knowledge?

If the people who do science are not representative of humanity — is the knowledge they produce fully representative of humanity’s questions?

Deeper Challenges

The Problem of Induction

Induction: drawing general conclusions from particular cases.

This is the fundamental reasoning tool behind all scientific laws.

The problem:

When you add water to anhydrous copper sulphate, it goes blue.

How many times must you repeat this before it becomes a law of nature?

What if on one trial it stays white?

  • Problem 1: No set standard for how many observations are enough.
  • Problem 2: We generalise to cases we have not observed — but science is supposed to be empirical.

No matter how many confirming observations you make, a counterexample is always possible.

Unobservables

Many central entities in science cannot be observed — even in principle:

  • Gravitational, electric, and magnetic fields
  • Energy (you can observe its effects, not energy itself)
  • Genes as units of inheritance (DNA is observable; the abstract unit is not)
  • Quarks — constituent parts of protons/neutrons; can never be isolated or observed alone

Empiricists / Anti-realists: Only what can be directly observed exists. Quarks, fields, and scientific laws are useful fictions — not real.

Most scientists: Quarks exist — it would be a miracle if quark theory made correct predictions but quarks didn’t exist.

Is Science Empirical in the Strict Sense?

To what extent is science actually empirical — accepting the existence only of things that can be observed?

If science is strictly empirical, quarks, fields, and laws don’t exist.

If science accepts unobservables, it is only loosely empirical.

But then — so are many other areas of knowledge.

Perhaps the strict sense of empiricism is an ideal, not a description. And if that’s right, science is not as unique as it claims to be.

Discussion Questions

Choose 2–3. Allow 2–3 min per question.

Question A — Perspectives and Objectivity

“We have seen that scientific observation is theory-laden, shaped by expectations, and subject to confirmation bias.

Is any scientific claim truly objective? Or is objectivity always a matter of degree?”

Follow-up: If complete objectivity is unachievable, is it still a useful goal? Does aiming at objectivity matter even if we never fully reach it?

Question B — Who Does Science?

“If the history of science has been dominated by men, aristocrats, and wealthy Western institutions, is the content of science itself biased?

Or does the method correct for the perspective of the knowers?”

Follow-up: Think of a scientific question that might have been asked differently — or answered differently — if the research community were more diverse.

Question C — Science and Its Limits

“The deeper we look, the more we find that scientific knowledge is: theory-laden, model-dependent, troubled by induction, and full of unobservable entities.

Does this undermine science — or does it reveal something important about the nature of knowledge in general?”

Follow-up: What do other AoKs (history, the arts, the human sciences) share with natural science here? What’s the same? What’s different?

Exit

Three Things to Leave With

  1. Perspectives exist in science — in theory, method, and interpretation. The question is not whether to eliminate them (impossible) but how to manage them. Peer review, replication, and diverse communities are the tools.

  2. Observation is not innocent — it is shaped by concepts, expectations, and technology. This is a challenge, not a scandal. The scientific community has mechanisms to catch and correct errors — but they are slow and imperfect.

  3. Science is a human activity — conducted by people embedded in societies, cultures, and power structures. Who does science shapes what science asks. The knowledge produced is real and reliable — but its agenda is not value-free.

Exit Ticket

Write 2–3 sentences — choose one:

Option 1: “Give a real or hypothetical example of confirmation bias in science. What mechanism should correct it — and how long might it take?”

Option 2: “What is the difference between ‘seeing-as’ and ordinary neutral seeing? Give an example from science and one from everyday life.”

Option 3: “Should the under-representation of women and minorities in science concern us for epistemological reasons (not just ethical ones)? Argue a position.”

Reference

Key Terms — Perspectives

Term Definition
Unity of science One world → one true unified theory all sciences converge on
Pluralism Multiple theories can co-exist; a patchwork quilt is acceptable
Falsificationism Scientists should try to falsify, not confirm, their hypotheses
Auxiliary hypothesis An assumption needed to run an experiment; can absorb a falsification
Null hypothesis (H₀) Hypothesis of no relationship between variables
Wave-particle duality Light exhibits both wave-like and particle-like properties depending on the experiment
Anomaly An observation that a theory cannot explain; drives science forward without immediately overthrowing the theory

Key Terms — Challenges

Term Definition
Selectivity of observation Choosing what to observe before the investigation — cannot be neutral
Theory-laden All observations are structured by prior concepts and theory
Seeing-as Perception is active: we see things as something, not neutrally
Observer effect The act of observing changes the system being observed
Probe effect Contact of measuring device with system changes it
Confirmation bias Tendency to favour evidence that confirms a hypothesis over disconfirming evidence
Background assumption An assumption needed to get an investigation started; can skew the inquiry
Problem of induction No matter how many observations confirm a hypothesis, a counterexample remains possible
Unobservables Theoretical entities that cannot be observed even in principle (e.g., quarks)
Empiricist Thinker who holds that only observable items exist; anti-realist about unobservables

TOK Connections

  • Knowledge and the Knower — How do the social identity of scientists shape what gets studied?
  • Core Theme: Knowledge and Responsibility — Who bears responsibility when confirmation bias leads to harm (e.g., N-rays redirecting medical research)?
  • Core Theme: Knowledge and Language — How did a mistranslation (canali → canals) shape decades of astronomical observation?
  • Human Sciences AoK — Confirmation bias, observer effects, and theory-laden observation are equally present in psychology and sociology — does this make those fields less reliable than natural science?
  • History AoK — How do we know about the historical exclusion of women from science? What sources do we use?
  • Ethics — Is it enough that individual scientists act well, or does the system need structural reform to correct for socio-cultural bias?