Whether it's stripes, plaids, or dancing pineapples, a pattern is something that repeats in a predictable way. However, scientists are less concerned with the fashion aspect of patterns than they are with stuff repeating in nature.
Yep, that's right. Nature has its own patterns. Gravity is a pattern, predators and prey are patterns, even chemicals react in patterns. The cool thing about these patterns is that we can use them to predict stuff. For example, a scientist can use what they know about patterns in predator and prey relationships between wolves and moose to hypothesize, which is really just a fancy word for predict, what will happen if the wolf population increases.
When scientists are doing science, they're always looking for patterns. First of all, these patterns tell us we're on the right track. If something weird happens once, well, it could just be a strange one-off mistake. But if it repeats over and over again, that weirdness would be a pattern. This is why you'll find scientists doing their experiments multiple times. Well, and because experimenting is fun, but they're really on the hunt for patterns in their data. The more data they have, the easier is to find those patterns, so it's super important for them to perform multiple trials, gather tons of data, and have huge sample sizes. Now, what causes these patterns is a different question that leads to, you guessed it, another experiment.
Secondly, patterns help scientists understand the natural world better. They help scientists organize and classify stuff. If you've ever tried to make a family tree of every species that has ever lived on Earth (like you do), then you know how important patterns in anatomy, DNA, and development are. If you've ever tried to keep all one hundred eighteen elements from running amuck, you know how important patterns in valence shells, reactivity, and states of matter are. And if you want to make sure that an asteroid cruising through space isn't going to invade Earth's personal space, you know how important patterns in orbital paths are.
Lastly, there are also patterns in data. This is why scientists love graphs so much. Aside from the fact that they're pretty, graphs help those patterns stick out like florals on stripes. Patterns in data can tell us about where earthquakes tend to occur, how many bugs a bird eats in a day, or at what temperature the tires on a car will melt. All pretty useful information if you ask us.
Patterns are everywhere in science, and they're super useful in helping us understand what's going on out there. So, keep that in mind as you're getting dressed—and don't be afraid to throw some polka dots in the mix.
The Mayans and Aztecs had chocolate, pirates have their booty, and scientists have evidence. Evidence is what scientists live, breathe, observe, and experiment for. How is evidence more important than chocolate or treasure? In the race to determine which scientist has the right explanation for some fancy phenomenon, the one with the most evidence wins. Only the prize here isn't some trophy, it's knowledge. We'd take that over a trophy any day.
Evidence is something we'll see pop up in all of the different parts of the scientific process. The evidence ball gets rolling when a scientist makes an observation. Evidence from that observation will guide them in making a hypothesis and will give the hypothesis context. For example, if a scientist is studying why birds fly south in the winter, they probably need to have some evidence that birds actually fly south in the winter so the other scientists don't put them on the Don't Believe 'Em List.
When the time to experiment comes around, collecting evidence is a super important process. How and what evidence is gathered can make or break an experiment. The experiment has to be well designed, so that the evidence will actually answer the experimental question. No, we can't just feed rats espressos for fun.
The scientist also has to actually know what they're doing when they collect the evidence, too. Not knowing how to use a mechanical pipette or measure in centimeters can result in all kinds of error that turn glorious evidence into garbage. Scientists also have to watch out for the dreaded random errors that can give some funky data, as well as their own silly mistakes. No, 2 + 2 does not equal 7.
One evidence-strengthening strategy scientists like to use is repetition. They do their experiment over and over and over and…you get the picture. This may seem tedious, but it sure gives them a lot of evidence. For example, let's say someone bet us $1 million that they could make a basketball shot from half court. What if we had seen them do it, just once? We probably wouldn't take that bet. However, what if we had seen them do it once, but had also seen them miss one hundred other times? Given this amount of evidence, we're willing to bet our collection of Star Wars Pez dispensers that that ball isn't making it through the hoop.
Same thing goes for scientific evidence. If scientists see a result was only produced once, they're definitely giving it the side eye. But if they see a result was produced a thousand times, now they're all up in its business.
Every major scientific theory is also backed by ridiculous amounts of evidence. This evidence may come from different scientists in different fields of study, but it all points toward the same conclusions.
The moral of the story is that evidence is everywhere and it's super important to scientists. If we're collecting evidence, we must remember to be careful to avoid errors and try to be as accurate as we can. If we're reviewing someone else's experiment, we have to look for that evidence and treat it like gold. Or chocolate.
They say that in life, change is the only constant. Go ahead, jot it down on your list of potential senior yearbook quotes. We'll wait.
Okay, so let's talk about change. Change is happening all the time, all around us. And when it comes to the scientific method, change is what we're looking for. For example, let's say we want to know if a certain fertilizer works. We're going to add it to our soil, in a controlled way of course, and see if it changes the way the plant grows. Obviously we're hoping for a positive change (who doesn't love tomatoes the size of cantaloupes?), but a negative one can be just as telling.
Then there's change's polar opposite, stability. Stability may not be as exciting as change, but it can still help us out with an experiment. For example, when we control our variables, some of them remain stable. With our fertilizer example, we will keep the amount of fertilizer each plant receives stable. The amount of fertilizer given will be our independent variable. Then we'll observe to see if differences in our independent variable affect our dependent variable, plant growth. We'll need to keep outside variables, like type of soil, temperature, and how much water and sunlight is given, stable as well. This allows us to be sure those stable variables didn't cause any hijinks with our experiment that would affect how our plants grow.
Stability can also tell us that whatever we were testing didn't produce any changes. Maybe that fertilizer we were working on produced tomatoes the exact same size as the tomatoes without fertilizer. Not the desired results, but at least now we know we need to redesign our fertilizer.
Change and stability are two big ideas that scientists are constantly looking for, trying to achieve, or using as evidence to explain something, so we're going to see them everywhere. We weren't kidding when we said change is so constant you can never step in the same river twice (make sure you quote us on that in the yearbook, it's S-H-M-O-O-P). Actually, we're pretty sure it was some Greek philosopher who said it, so you might want to change that.
Have you ever tried to order takeout on a rotary phone? Or use a Thomas Guide for directions? Have you ever even heard of a rotary phone or a Thomas Guide? No? Well, you have a scientist to thank for that.
At one point, these were all pretty serious first-world problems. Thankfully, scientists are professional problem solvers. And they've got a pretty spectacular tool in their belt called the scientific method.
Scientists can use the scientific method to solve all kinds of problems, from how to keep us flu-free to feeding millions of hungry people in undeveloped countries. Of course it all starts with an observation, or more likely an annoyed groan. "Ugh, my socks are stuck to my pants, again?" Then comes the hypothesis. What could possibly be done to remedy this problem?
After that, there's a lot of experimenting. Very rarely does a scientist solve a problem on the first try. This means they've got to get back to the drawing board, revise their experiment and try all over again. And again. And again. Then when they've finally got an answer to the world's problems, they can pat the scientific method on the back and enjoy not having to deal with serious problems, like world hunger or static cling.
The gist here is that the scientific method can be used to solve problems of all magnitudes. In fact, we've been using the scientific method to solve problems our whole lives. Need to know how to build the tallest block tower ever? We're going to need to do quite a few experiments with gravity to find the combination of blocks we need to achieve that impressive feat of block construction. This is just like when scientists try to figure how to fuse atoms or grow a human ear on the back of a mouse. It's a lot of trial and error, but it's all worth it in the end.
Oh, cause and effect. How we love you in the science world. Cause and effect is the backbone of the scientific method; it drives everything from our hypotheses to our conclusions in an experiment. The job of the scientific method is to tell us if a cause produces an observed effect.
For example, let's say we want to find out if peanut butter will cure athlete's foot (we're doing the experiment for a friend, we swear). We're hypothesizing that the peanut butter will kill the athlete's foot fungus and the symptoms will disappear. Did you catch the cause and effect there? In case you were so grossed out you missed it, the cause would be the peanut butter, the effect would be no athlete's foot symptoms.
Okay, so now that we've given our hypothesis the cause and effect treatment, we need to perform our experiment and see if our proposed cause and effect is an actual cause and effect. Any volunteers? Anyone? Hello? Bueller? Bueller?.
All right, we'll just make up some hypothetical results. Let's say after two weeks of applying peanut butter to the, er, affected area, we observe that there is no change in the symptoms, but the area is quite moisturized and smells like a Nutter Butter. We can conclude that peanut butter did not cause the athlete's foot symptoms to disappear, but may have caused the foot odor to die down.
We do have to be careful about identifying something as a cause of something else. Sometimes what seems like a cause is actually a correlation. A correlation just means there's some kind of relationship between two variables. No, they're not asking each other to prom. We usually describe this relationship as positive, negative, or no relationship at all. Okay, so maybe this does describe prom.
Anyhoo, back to correlations. Let's say Mama Shmoop bakes us a batch of her famous snickerdoodles every week. Every time she brings them over, it rains. We could say there is a positive correlation between rain and snickerdoodles. What we can't say is that the snickerdoodles caused the rain. At least not without evidence to back it up. Unless we can get some scientific proof that Mama Shmoop's cookies are causing some major meteorological mayhem, it's just a coincidence that these delectable treats appear when the rainclouds do.
Guess what. Cause and effect isn't just important in science. We'll see this concept creeping into all sorts of different subjects, and even into our lives. What caused World War II and what were the effects? What caused us to miss curfew (and let's not talk about the effects)? It doesn't matter what field we're studying, being able to see the relationship between cause and effect is going to help us understand it like a pro.
Science studies stuff from an itty-bitty atom-small scale to a massive universe-big scale, and everything in between. What exactly is a scale, though? Aside from being the thing we avoid standing on after the holidays, a scale is a range that shows us how things relate to one another. For example, we can use a scale to compare size, strength, quality, or even time.
Before a scientist can hop on the experiment train, they need to understand exactly what it is they're studying and on what scale they're going to be studying it. Let's take bacteria, for example. They're small, usually thought of as gross, and they're all over everything. Let's say our friend the bacteriologist wants to know more about these little guys. The bacteriologist is going to first have to decide what scale she wants to look at.
Does she want to stick to a small scale and study the bacteria themselves? Or does she want to zoom out a bit and understand how bacteria work in the small intestine? Or should she zoom out even farther to understand how intestinal bacteria differ in various countries throughout the world? Understanding the scale we want to look at gives us a better chance of answering our experimental question and not getting lost in the details or stuck in the stars.
Scale doesn't just have to do with size, either. We could be talking about a timescale or how much energy something uses. Think about studying the life cycle of a tsetse fly versus studying all of evolution. Or the energy used by a 40-watt light bulb versus a star. Those are some very different scales.
In order to understand scale, we might need to throw a little math at it. This is where proportion and quantity come in. Proportion is the relationship between parts to each other or to a whole. For example, the proportion of the pizza we ate last night is relatively small if we compare it with all of the pizza in the whole world. Proportion is awesome because it allows scientists to zoom in and out relatively easily. They can use proportions to apply what they've learned on a small scale to a larger scale situation. Or if big to small is their jam, proportion works there too.
Then there's quantity. Quantity is basically how much of something we have. This is a favorite observation to make, mostly because knowing how much of something you have is pretty important for understanding scale and proportion, and eventually studying changes. Imagine that we were asked to see how warmer temperatures have affected how many salmon are born in a specific stream this year. Now imagine we don't know how many salmon were born last year. Kind of makes answering how the birth rate has changed, um, impossible.
Understanding scale, proportion, and quantity are crucial to scientists as they conduct their experiments and attempt to answer questions. After all, we wouldn't use a Band-Aid to cover a bullet hole; why would we use magnifying glass to study the sun?