Chapter IaS3: How Are Scientific Explanations Developed?

Welcome to IaS3: How are scientific explanations developed? This chapter is part of Ideas about Science in your OCR GCSE Combined Science B (Twenty First Century Science) course. Unlike topics that only show up in one subject, Ideas about Science is tested across all your Biology, Chemistry, and Physics papers!

Don't worry if scientific terminology sometimes feels confusing. In this guide, we will break down how scientists observe the world, build models, test their ideas, and check each other's work in simple, step-by-step notes.


Science usually starts with observations and data collection. When scientists look at tables of data or graphs, they look for patterns.

What is a Correlation?

A correlation is a pattern or relationship in data where two variables change together.

• Positive correlation: As one variable increases, the other variable also increases (for example, as temperature rises, ice cream sales increase).
• Negative correlation: As one variable increases, the other variable decreases (for example, as car speed increases, travel time to a destination decreases).

The Golden Rule: Correlation Does NOT Mean Causation!

This is one of the most common mistakes in GCSE exams. Just because two things happen at the same time does not mean one caused the other.

A cause-effect link (causation) means that a change in one variable directly results in a change in another variable.

Why might two variables correlate without one causing the other?
1. Coincidence: The two variables just happen to change together purely by chance.
2. A Shared Third Factor: A third, hidden variable is causing changes in both. For example, higher shoe size correlates with higher reading ability in children. Larger shoes do not make children read better! The third factor is age—older children have larger feet and can also read better.

Examiner Tip: When an exam question shows you a graph showing a correlation and asks if Factor A causes Factor B, always state that "correlation does not prove causation" and suggest that a third factor or further testing is needed.

Key Takeaway: A correlation suggests a possible link, but only controlled experiments with a proven mechanism can confirm a direct cause-and-effect link.


2. Scientific Models: Making the World Easier to Understand

The universe can be too tiny (like atoms), too massive (like galaxies), or too complex (like global weather) to study directly. That is why scientists use models.

What is a Scientific Model?

A model is a representation used to solve problems, make predictions, and develop scientific explanations for phenomena.

The Four Types of Models You Need to Know

OCR specifies four main types of models that you must recognize:

1. Representational Models:
These are simplified drawings, diagrams, or physical replicas that represent a real object or structure.
Examples: A diagram of a plant cell, a circuit diagram using standard electrical symbols, or a plastic model of a DNA double helix.

2. Spatial Models:
These show how objects or particles are arranged in 2D or 3D space.
Examples: A 3D diagram showing the arrangement of sodium and chloride ions in a crystal lattice, or a scale diagram of our solar system.

3. Descriptive Models:
These use words, flowcharts, or step-by-step descriptions to explain a process or system.
Examples: A diagram explaining the stages of the water cycle (evaporation, condensation, precipitation), or a flowchart showing how carbon cycles through an ecosystem.

4. Computational / Mathematical Models:
These use mathematical equations or computer simulations to calculate outcomes and make predictions.
Examples: Using the equation \(F = ma\) to calculate how a force affects acceleration, or running supercomputer simulations to forecast climate change over the next 50 years.

Key Takeaway: Models help us simplify, visualize, and calculate things that are otherwise impossible to observe directly.


3. Developing Theories: How Ideas Grow and Change

Hypothesis vs. Prediction (Do Not Mix These Up!)

Students often lose marks by using these two words as if they mean the exact same thing:

• Hypothesis: A tentative explanation for an observation (the "why").
• Prediction: A statement of the expected outcome of a specific test if the hypothesis is correct (the "what will happen").

Example:
• Observation: Plants near the window grow taller than plants in the dark corner.
• Hypothesis: Plants grow faster because light provides energy for photosynthesis.
• Prediction: If we place seedlings under lamps with higher light intensity, then they will gain more mass over two weeks than seedlings under dim lamps.

Creativity in Science

Science is not just about memorizing facts; it requires great creativity and imagination to come up with new explanations. For example, Isaac Newton had to think creatively to propose that the same invisible gravitational force pulling an apple to the ground also keeps the Moon in orbit around Earth.

Scientific Explanations are "Provisional"

What does provisional mean? It means accepted for now, based on the current evidence available.

Scientific knowledge is never set in stone. When new experimental tools are invented (such as better microscopes or particle colliders), scientists discover new data. If the current model cannot explain this new data, the theory must be modified or replaced entirely.

Classic Examples:
• Atomic Theory: Started as solid spheres (Dalton), changed to the plum pudding model (Thomson), then the nuclear model (Rutherford), and later the electron shell model (Bohr).
• Genetics: Gregor Mendel’s early ideas about inherited "units" were modified once scientists discovered DNA and the structure of genes.

Key Takeaway: Hypotheses explain "why"; predictions state "what will happen". Scientific explanations are provisional and evolve whenever new evidence is uncovered.


4. The Scientific Community and Peer Review

When a scientist makes a new discovery, they cannot simply announce it as a proven fact. It must go through a strict checking process.

What is Peer Review?

Peer review is the process where new scientific research and claims are independently evaluated by other scientists working in the same field before the work is published in a scientific journal.

Why is Peer Review Essential?

1. To check validity: Ensures experiments were designed fairly and measurements are accurate.
2. To assess originality and quality: Checks that the work provides meaningful insights and meets high standards.
3. To detect errors and false claims: Catches fraudulent data, mathematical errors, or biased conclusions.
4. To establish scientific consensus: Helps scientists agree on which theories are supported by strong evidence.

Peer-Reviewed Journals vs. Popular Media

Be careful when reading science in everyday life!
• Scientific Journals: Papers have been peer-reviewed and scrutinized by experts.
• Popular Media (Newspapers, TV, Social Media, Websites): These are not peer-reviewed. Media reports often oversimplify complex findings, use sensational headlines to attract attention, or present biased views.

Examiner Tip: When defining peer review, always state that the research is checked by "other scientists working in the same field" to ensure "validity". Just writing "checked by someone else" will not get full marks!

Key Takeaway: Peer review keeps science trustworthy by having independent field experts check research before publication.


5. Working with Data: Outliers and Measurement Terms

In all your science exams, you will be asked to analyze experimental data. Here are the core data terms you must master for IaS3:

What is an Outlier?

An outlier (or anomalous result) is a data point that is significantly different from the rest of the results in a repeated dataset.

How to handle an outlier:
• An outlier should not be included when calculating the mean (average).
• However, an outlier should only be completely discarded if there is a clear experimental reason to reject it (such as a known spill, misread scale, or incorrect timing). Otherwise, the trial should be repeated to investigate why it happened.

Key Measurement Terms

• Accuracy: How close a measured value is to the true value.
• Precision: How close repeated measurements of the same quantity are to each other (spread of values around the mean).
• Repeatability: The closeness of agreement when the same investigator uses the same equipment and method to obtain results.
• Reproducibility: The closeness of agreement when a different investigator or different equipment conducts the investigation.

Key Takeaway: Exclude outliers from mean calculations, and identify whether differences in results come from human error or actual scientific variation.


Quick Review Checklist

Before sitting your exam, check that you can answer these questions with confidence:

✓ Can you explain why a correlation on a scatter graph does not automatically prove causation?
✓ Can you name and give an example of each of the 4 types of models (Representational, Spatial, Descriptive, Computational/Mathematical)?
✓ Can you state the difference between a hypothesis (explanation) and a prediction (testable outcome)?
✓ Can you explain why scientific theories are provisional and give an example of an evolving theory (like the atom)?
✓ Can you accurately define peer review and explain why newspaper articles are not peer-reviewed?
✓ Do you know what to do with an outlier when calculating a mean value?