Study Guide

# Nature of Science - Common Mistakes

## Common Mistakes

### The Scientific Method

The biggest mistake we should avoid with the scientific method is thinking of it as a something that happens in a straight line. Yes, it's easier to think of the scientific method as a bunch of steps, and that's why we'll hear people talking about "the" scientific method like a DIY project. However, out in the science world, the scientific method is less like running the 100-meter dash and more like something out of Cirque du Soleil.

Scientists are constantly observing, asking questions, experimenting, observing again, asking more questions, designing more experiments…phew! So instead of thinking of the scientific method as a process with a single start and end point, think of it as a bunch of twisty-turny roads that are all connected to one another.

### Collecting Data

It's easy to get tripped up on the vocabulary terms we covered here. Seriously though, could qualitative and quantitative be any more similar? Here's the trick: just remember "quant" sounds like "quantity," which has to do with numbers. "Qual" is like "quality," sort of like how we think our subtle sarcasm is our best quality. Our eyes aren't bad either.

To make sure you hit accuracy and reliability on the mark every time, check out this bulls-eye visual. We like it better than writing out the definitions one hundred times each.

### Graphing Data

Let's be honest. There are a lot of areas we could go wrong with a graph. Missing titles or labels, forgetting to put the units, a confusing key…yikes. However, one of the worst graphing mistakes we can make is messing up the scale. This can change how our entire graph looks and how our data are interpreted. Remember, our data have to fit within the graph and our scale has to stay the same across the entire axis (unless we have an axis break). We definitely don't want our graph looking like this.

### Analyzing Data

Some common mistakes scientists make when analyzing graphs is assuming correlation means causation. Just because murder rates increase as ice cream sales go up does not mean ice cream causes more murders. To actually show causation, we need a well-designed experiment. Otherwise, we're just speculating.

### Error

• Human error: a mistake we make that can be avoided by following proper lab techniques and generally being careful
• Equipment error: mistakes made by faulty or uncalibrated equipment. These can be avoided by making sure equipment is in good, working condition and calibrated according to the manufacturer's specifications
• Random error: errors that can't really be blamed on anything specific, which makes them tough to avoid. We can try to anticipate them and plan accordingly, but sometimes if you gotta sneeze, you gotta sneeze.

### Ethical Issues

Running a science experiment and running a marathon have one thing in common: they're both really hard. Scientists do everything they can to design and run the best possible study with limited time, budget, and resources, just like a marathon runner is trying to make it to the finish line on a Power Bar and a pair of Nike's. Add to that a bunch of ethical considerations that can make the perfect experiment super tricky, if not impossible, and scientists have got a lot to keep track of.

Are there shortcuts? Sure. Just like our marathon runner can hop on a bus and skip few miles of the race, our scientist might be tempted to take a quicker, less ethical route to answer their experimental question. But that line of thinking gets no sympathy, from us or the wider scientific community. Scientists can't ethically replicate an unethical experiment, which means someone broke the rules for no benefit whatsoever. And nobody wins.

### Scientific Models

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.

### Communicating Results

A major mistake scientists make in communicating is not being clear in how they conducted their experiment. Remember that other scientists may try and repeat this experiment, so if our procedures are confusing, they may as well be written in Sanskrit. Don't believe us? Write out the instructions for making a peanut butter and jelly sandwich and have someone who's never made one follow them. If we don't write out each step clearly and in detail, we may need to order in lunch.

When we're writing our procedures, we should be as clear as possible, include pictures or diagrams when we can, and have someone else read it to make sure it makes sense to someone on the outside of our heads. If your lab procedures are less like a glass of water and more like a cement smoothie, head on over to the Shmoop Essay Lab and we'll help you clear things up right quick.

### Communicating Results, Take Two

We're constantly getting smacked in the face with scientific studies making all sorts of claims, like shampoo causing cancer, or that cramming for a test is more effective if you've had a Red Bull. The mistake is for us to blindly believe the news reports on these studies without knowing all the facts.

Unfortunately, what we rarely hear after the phrase "studies say" are the important details. Was that shampoo study performed on mice or humans? Was the Red Bull study funded by Red Bull? Did they use appropriate science techniques and sample sizes? Did they analyze their data correctly and without bias? Who knows? By the time we hear about it on the news, it's already been generalized into an eye-catching headline that we can impress our friends with at a party, while the science stuff is buried in a drawer somewhere.

In the science world, scientists would never, ever, ever accept the results of a single study as fact. First they'll scrutinize how the study was performed and the data that were analyzed; then they'll wait for confirmation from other studies. Even then, they'll always be giving it the side-eye. Science is a long, slow process that requires a lot of double, triple, and quadruple checking before new conclusions are accepted as valid. So, don't believe everything that comes after "studies say," unless it's, "Studies say students who use Shmoop are awesome." That's totally legit.

Just kidding. That was a trap; we made that up to keep you on your toes. We do think you're awesome though. Stay skeptical, friends.

### Law vs. Hypothesis vs. Theory

Understanding theories, laws, and hypotheses can get confusing. Especially if you don't have Shmoop on your side. One of the biggest uh-oh's people make when it comes to understanding theories, laws, and hypotheses is thinking that they are hierarchical.

What we mean by that is that lots of people think that if we add more evidence, a hypothesis can become a theory, and a theory can graduate into a law. This isn't really the case, though. First of all, laws aren't the boss when it comes to scientific explanations. Scientists actually consider theories to be top dog when it comes to explaining stuff.

Secondly, we use laws, hypotheses, and theories to explain different things. For example, a law explains something specific, usually using a mathematical equation, and is supported by a lot of experiments and evidence. A hypothesis is an explanation for a specific phenomenon that can be tested by an experiment. A theory is an explanation, also supported by lots of experiments and evidence, for a broader concept or set of supported hypotheses.

Comparing these three is sort of like comparing a hippo, a huckleberry, and a housefly. The hippo isn't going to turn into a huckleberry, which certainly isn't going to turn into a housefly. At least not without us having to make some major changes to some pretty important theories.

### Science and Technology

Science and technology are two words that are very closely related. But do they mean the same thing? Not really. Even though they're often used interchangeably, science and technology have different purposes and spit out different products. For example, science gives us scientific knowledge based on evidence from observations or experiments. Technology gives us a solution to a problem, like how to get our watermelon from point A to point B.

These two fields do lean on each other, using each other's advances to help advance themselves. However, science is all about collecting evidence through experiments and observations and using that evidence to explain something. Technology attempts to create new stuff by designing, building, testing, and then mass-producing their invention. Of course, scientists do use some of these inventions, and some of these inventions are based on discoveries made by scientists. Which makes science and technology kind of like brother and sister. Or aunt and uncle? First cousins, one removed? Hold on, we'll get the family tree...