Naomi Oreskes: Why we should trust scientists

Why Should We Believe in Science?

This section explores the reasons why people may not believe in scientific information and introduces the concept of belief in science.

Belief in Science vs. Faith

• Scientists contrast science with faith and emphasize that belief is separate from science.

• Religion is often based on faith or beliefs, such as Pascal's wager, which involves making a leap of faith.

Scientific Claims as a Leap of Faith

• Most scientific claims are a leap of faith for both the general public and scientists outside their own specialties.

• Scientists themselves have to make a leap of faith when accepting claims from other scientists.

The Role of the Scientific Method

• Many people were taught to believe in science because of the scientific method.

• The textbook method, known as the hypothetical deductive method, involves developing hypotheses, deducing consequences, and observing them in the natural world.

Examples of Scientific Confirmation

• Albert Einstein's theory of general relativity predicted that light would bend around massive objects like the sun. This prediction was confirmed during an experiment in 1919.

• The textbook model suggests that confirming predictions through observation guarantees the truth of scientific claims.

Why Do Scientists Believe Each Other's Claims?

This section delves into why scientists believe each other's claims and whether we should also trust those claims.

Trusting Other Scientists' Claims

• Scientists accept each other's claims despite not being experts outside their own fields.

• Geologists cannot determine vaccine safety, chemists are not experts in evolutionary theory, and physicists cannot definitively say if tobacco causes cancer.

Making a Leap of Faith

• Even scientists have to make a leap of faith when accepting claims from other scientists outside their own areas of expertise.

Reasons to Believe Scientific Claims

• It is argued that we should believe scientific claims, but not solely because of the scientific method.

• The reasons for believing in science go beyond the textbook model and the idea that observation guarantees truth.

Problems with the Textbook Model

This section highlights three problems with the textbook model of scientific confirmation.

Logical Problem: Fallacy of Affirming the Consequent

• Just because a prediction comes true does not logically prove that the theory is correct.

• The example of the Ptolemaic model, which accurately predicted planetary motions but was later proven false, demonstrates this problem.

Practical Problem: Auxiliary Hypotheses

• Scientists often make assumptions or auxiliary hypotheses that may influence their claims without being fully aware of them.

Limitations of the Deductive-Nomological Model

• The textbook model, also known as the deductive-nomological model, assumes that hypotheses are laws of nature that cannot be broken.

• However, this model has several problems and is ultimately incorrect.

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Stellar Parallax and Scientific Models

In this section, the speaker discusses the concept of stellar parallax and its relation to the Copernican model. They also explain how scientists make predictions based on models and the challenges they face in detecting stellar parallax.

Stellar Parallax and the Copernican Model

• Astronomers attempted to detect the motion of Earth around the sun through a concept called stellar parallax.

• Stellar parallax refers to the apparent shift in position of a star when observed from different points in Earth's orbit.

• The lack of detection of stellar parallax initially led many to believe that the Copernican model was false.

Challenges in Detecting Stellar Parallax

• Astronomers made incorrect assumptions about the size of Earth's orbit and the sensitivity of their telescopes.

• The small size of stellar parallax made it difficult to detect until advancements in technology during the 19th century.

• Scientists also faced challenges due to discrepancies between textbook models and real-world observations.

Inductive Science and Modeling

This section explores two approaches used by scientists: inductive science and modeling. It highlights Charles Darwin as an example of inductive science and introduces Henry Cadell's physical model for understanding mountain formation.

Inductive Science

• Inductive science involves starting with observations rather than theories or hypotheses.

• Charles Darwin collected data without a specific hypothesis, which later led him to develop his theory of natural selection.

Modeling

• Scientists often use models to explain causes. These models can be physical or computer simulations.

• Henry Cadell built a physical model to demonstrate how rocks could be folded and create mountain-like patterns when compressed from the side.

• Nowadays, scientists prefer computer simulations as models, which are made using mathematics.

Modeling Climate Change

This section focuses on modeling climate change and understanding the factors that contribute to global warming. It emphasizes the importance of greenhouse gases in explaining observed temperature increases.

Modeling Climate Change

• Scientists use computer simulations to understand the causes of climate change.

• The increase in Earth's temperature over the last 150 years, particularly in the last 50 years, is well-documented.

• Computer simulations consider various factors such as air pollution, volcanic dust, solar radiation, and greenhouse gases.

• A model that includes all these variables can accurately reproduce the observed temperature measurements.

• Greenhouse gases play a significant role in driving the observed increase in temperature.

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The Method of Science: Anything Goes

This section discusses the method of science and how scientists make decisions based on evidence.

Scientists and the Method of Science

• Scientists use a variety of methods in their work, making science a creative field.

• The question arises: How do scientists determine what is right or wrong?

• Scientists judge evidence collectively and subject it to scrutiny.

• Sociologist Robert Merton coined the term "organized skepticism" to describe how scientists scrutinize data.

• The burden of proof lies with those making novel claims, making science intrinsically conservative.

• Major changes in scientific thinking are relatively rare.

Consensus and Scientific Knowledge

• Historians focus on consensus as an important aspect of scientific knowledge.

• Scientific knowledge is the consensus reached by experts through organized scrutiny.

• Science can be seen as a jury of experts who have different choices: true, false, more evidence needed, or unanswered questions.

Appeal to Authority and Collective Wisdom

• Science appeals to authority but not based on individual expertise.

• The authority comes from the collective wisdom and knowledge of all scientists working on a particular problem.

• Scientists have a culture of collective distrust and rely on evidence-based reasoning.

Trust in Science and Technology

This section explores trust in science and technology, drawing parallels between them.

Trust in Technology

• Trust in technology is built upon its reliability over time due to accumulated effort.

• Modern automobiles are reliable because they are the result of years of work by countless individuals.

Trust in Science

• Trust in science is similar to trust in technology; it is based on experience.

• However, blind trust should be avoided, just like with anything else.

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