Study Guide

Nature of Science - Scientific Models

Scientific Models

What do Heidi Klum and Darwin's Theory of Evolution have in common?

Give up? They're both models. While Heidi models the latest trends in pleather and plaid, Darwin Theory of Evolution models the latest trends in survival and reproduction. We think these two actually would have gotten along quite nicely.

These Models Do More Than Pose

Models are kind of a big deal in science. See, most the time, scientists are looking at stuff that's really big or really small or that happens really fast or really slow. Models help us to study that stuff without having to wait thousands of years or build a space ship that can travel at the speed of light. Pretty rad, huh?

So what does a scientific model look like? A scientist would tell us they look like the most beautiful thing in the world. While we would have to agree, let's go with something more specific. Models are basically anything that tries to explain how something in the natural world works. They can be in the form of a mathematical equation, a graph, a picture or diagram, a description, or a 3D representation.

One of the best parts of a scientific model is its power of prediction. Scientists can use these models to make a pretty good guess as to how something is going to turn out without having to wait around for it to actually happen. This is really awesome when we want to predict the path of an asteroid headed toward Earth, or what the human population will be in 2416, or what will happen if we don't zip up that hole in the ozone layer.

The Making of a Super Model

The cool thing about models is that we can create them to help us learn about pretty much anything. When scientists make models, they consider how the model will be used and what aspects of the phenomenon they're modeling they'll want to include. If they're modeling a system, they'll need to set some boundaries for the system to keep it as simple as possible, otherwise they may start by modeling the carbon cycle and end up modeling the whole universe. No. Thanks.

We can make models of stuff we already know about, like the structure of a flower or how blood flows through the circulatory system. However, one of the coolest things models do is help us predict stuff we've never seen before. So, how can we make a model based on something that's never happened? Well, scientists gather up all the related information they do know and pour that into their model to help them predict what they don't know.

For example, let's say scientists want to create a model that predicts what would happen if the polar ice caps melt. This phenomenon hasn't happened before (at least not when humans were around to blog about it), but we do have mountains of data on how ice behaves. We know all about ice's melting habits, how it reflects sunlight back into the atmosphere, and how that reflection affects Earth's temperature. We also know how fresh water affects the density of salt water, how different densities of water affect the temperature and movement of water, and how currents affect local climates. Scientists even have data on weather patterns and can also take core samples from the ice to find out historical patterns from way back before anyone was around to write down that it was sunny with a chance of wind.

Yes, this is a lot of information. And it's just the tip of the, er, iceberg. Throw in all the other stuff we know about ice, currents, and climate and our model can get pretty complex, pretty quickly. While all of these considerations may make your head spin, they're really helping to make our model a more accurate predictor of what's to come. Scientists do have to be careful though. Models are prone to mistakes if things get too complicated. Just try juggling a ball, a tea set, and hedgehog and let us know if you agree.

Let's Hit the Runway

Here's a front row seat to watch some of scientists' most impressive models strut their stuff. Enjoy.


  • The Bohr Model: This gem comes from the mind of Niels Bohr and describes atoms as negatively charged electrons spinning in circular orbits around a positively charged nucleus. It's not a perfect representation of how electrons do their dance, but take a chemistry class and you and this model will be besties in no time.
  • The Double Helix Model of DNA: After a lot of failed attempts, James Watson and Francis Crick got some help from Rosalind Franklin in the form of an X-ray crystallography shot of DNA. This gave them the information they needed to create their model of DNA, which showed two intertwined strands with sugar phosphate backbones and nucleotides that paired up according to a specific pattern.
  • The Lotka-Volterra Equations: These mathematical equations model how two species interact when one is the predator and one is the prey. This model allows us to predict how the population sizes of each species might change over time. Since there are so many variables involved in population size, the Lotka-Volterra equations do make several assumptions, like the prey population can always find food and the environment doesn't change to give the predator or prey an advantage, to keep things simple. We're not sure what these equations would tell us about the Roadrunner and Coyote's relationship, though.
  • The Billiard Ball Model of Atoms: John Dalton described atoms as a solid, unbreakable unit, much like a billiard ball. These units were the smallest part of an element, and they had different masses depending what element they made up. Dalton's model wasn't perfect, but it inspired other scientists to come up with new models, like the Plum Pudding Model. Yum.
  • The Hardy-Weinberg Equilibrium Equation: This bit of mathematical modeling uses five simplifying assumptions that make it easy to predict allele and genotype frequencies in a population where evolution isn't happening. We can then compare the actual allele frequencies to the allele frequencies we would expect if the population were in equilibrium. If the frequencies are different, we know at least one of those assumptions isn't being met and evolution is doing its thing.

Models Are Human Too

Models aren't perfect. Yep, just like the pretty lady on the cover of the magazine has probably had a little help from an airbrush, scientific models have their own limitations. To keep a model simple enough to use, scientists often have to leave out some of the little details or make assumptions that may not be true in actuality. If we try and include every single thing that could affect our model, we might end up with a mess that's too complicated to use.

Since models aren't perfect, they're constantly being revised and updated to include new findings. Hypotheses and theories are examples of models that are always getting a fresh coat of paint as we find out more about them. As these models get updated, so does our knowledge and understanding of the natural world. We wish we could say the same for our fashion sense.

Common Mistakes

When working with models, it's important to choose the right model for the job. For example, it would be pretty tough to sum up Einstein's Theory of Relativity in a graph. Or to show how a volcanic island chain forms using a mathematical equation. When we're out there science-ing, we want to make sure to pick a model that makes the phenomena we're studying easier to understand, not harder.

Brain Snack

NASA has a pet named FIDO. You won't find it befouling any fire hydrants, though. It's a model of the Mars Exploration Rovers that scientists can use to practice with. For example, before they send a command to the rovers on Mars, they can practice the command with FIDO to make sure the rover gets the right memo. This is a great use of a model to make sure they don't accidentally drive the Mars rovers off of a Martian cliff. Because that would be super awkward.

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