Scientific Models: Definition & Examples

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  • 0:01 What Is a Scientific Model?
  • 0:59 Visual Models
  • 1:47 Mathematical/Computer Models
  • 2:59 Revising Models
  • 4:16 Lesson Summary
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Lesson Transcript
Instructor: David Wood

David has taught Honors Physics, AP Physics, IB Physics and general science courses. He has a Masters in Education, and a Bachelors in Physics.

What is a scientific model and how are they used? Learn about the different types of scientific models, including visual, mathematical, and computer models, and discover some real-life examples of each.

What Is a Scientific Model?

When you think of the word 'model,' what comes to mind? Maybe it's a tall, thin person wearing designer clothes at a fashion show. So what's a scientific model? A tall, thin man or woman modeling a lab coat and goggles? Unfortunately a scientific model is a bit more abstract.

A scientific model is a representation of a particular phenomena in the world using something else to represent it, making it easier to understand. A scientific model could be a diagram or picture, a physical model like an aircraft model kit you got when you were young, or a computer program or set of complex mathematics that describes a situation. Whatever it is, the goal is to make the particular thing you're modeling easier to understand. When we do that, we're able to use it to predict what will happen in the future. For example, predicting what will happen as our climate changes would be easy if we could make a fully accurate model of the atmosphere.

Let's talk about the various types of scientific models, and discuss how scientists adapt and change them over time.

Visual Models

Visual models are things like flowcharts, pictures, and diagrams that help us educate each other. They are the ones non-scientists have most experience with. In an office you might create a flowchart that describes the work that you do. Maybe orders come in by phone, and that information gets transferred to both the warehouse, and the membership department. If you include every input and output, that flowchart is an example of a visual model.

In science, visual models are often useful as educational tools, say in a classroom or from a scientist to a colleague. For example, a visual model can show the main processes that affect what the atmosphere is made of. No matter how clever and educated you might be, diagrams are extremely helpful in explaining how the world works. They can describe abstract concepts, and show things that would be too tiny or too gigantic to see with our own eyes.

Mathematical/Computer Models

Scientific models are often mathematical models, where you use math to describe a particular phenomenon. For example, you might notice that the force of gravity on an object is equal to its mass multiplied by the strength of the gravity field. When you put all your gravity equations together, you get an overall model of gravity that was first created by Newton.

But humans have their limits. Those mathematical equations that Newton came up with can be quite confusing. It's fine when you're learning about simple situations in a science lab, but what about the real world? Using Newton's laws to explain the flow of a river over land is harder than you might think.

You'd need to consider rock and soil types, their friction and saltiness, and how the water flows around plants and various random shapes of rock. It certainly isn't easy, so to explain it fully you can use computer models, which are capable of complex calculations and animations. Inputting everything we know about gravity and forces into computers allows it to figure out what will happen far more quickly than any human could.

Mathematical and computer models are used to predict all kinds of things. Like how climate change might progress, or what might happen if an asteroid hits the earth. They're also used to simulate car crashes, or to model fire and smoke for safety studies or even Hollywood movies.

Revising Models

A model is by definition imperfect. It only represents something in the world in a way that lets us make predictions. But the real world sometimes shows us that we have more to learn. Scientific models are constantly being changed or updated when we get new data. If we find data that doesn't fit with our previous models, then someone has to figure out what went wrong and make improvements.

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