Nature of Science Study Questions

Bring on the tough stuff.

1. What does it mean for a hypothesis to be falsifiable?

2. What makes for accurate data? For precise data?

3. What kind of information is included in the legend of a graph?

4. What's the relationship between correlation and causation?

5. What are the three main types of error?

6. What is an IRB, and what does it do?

7. What are some types of scientific models?

8. How does peer review help improve our scientific results?

9. What are the differences between laws, hypotheses, and theories?

10. What's the relationship between science and technology?

Answers

1. What does it mean for a hypothesis to be "falsifiable"?

A falsifiable hypothesis is a happy hypothesis. Okay, while that is true, it's not really a definition. Something being falsifiable means that it can be shown to be incorrect. A particular hypothesis might not actually be incorrect, but the possibility exists that an experiment or observation will contradict it so hard, it'll practically toss itself into the trash.

2. What makes for accurate data? For precise data?

Accurate data are all aimed at the bullseye, while precise are all hitting around the same value. If our data are accurate but not precise, we'll be in the neighborhood of the correct value but will have a lot of uncertainty about what it exactly is. Precise but inaccurate data look beautiful…right up until you go to check them and go "These make absolutely no sense." And if the data are both inaccurate and imprecise, well, back to the drawing board.

3. What kind of information is included in the legend of a graph?

First, we mark whether it's the 1st or 50th figure we've used today. Then we just summarize what the graph is showing, where the data came from, and any relevant statistics. Basically, it's a quick, one-stop shop for anyone who wants to know what this graph is about, but doesn't have time to actually look at it.

4. What's the relationship between correlation and causation?

Let's spit out some definitions first. "Causation" refers to one event causing another one. Like when we eat two buckets of ice cream on a Saturday afternoon, then we spend all of Sunday in bed with a stomachache. The first event causes the second. If two events are correlated, though, that means there's some kind of relationship between them, but not necessarily a cause-effect relationship. In some cases, the correlation makes it look like there's some kind of causation, but there actually isn't. For instance, if we notice that we get a lot more stomachaches in the summer, then we might think there's something about summertime that gives us cramps. In reality, though, we're just going on a lot more ice cream binges when it's hot out.

5. What are the three main types of error?

Human error, equipment error, and random error. Whether the fault is in us or in our stars, we'll need to correct for them the next time we run our experiment.

6. What is an IRB, and what does it do?

An IRB stands for Institutional Review Board, and they're a big part of defending human test subjects from harm. When a scientist wants to do an experiment involving humans, they have to get approval from their local IRB first. If the Board doesn't think the risks of the study are worth its potential benefits, or if there are some ethical issues with its design, then they'll pull out their NO WAY rubber stamp and block the experiment from being conducted.

7. What are some types of scientific models?

Scientific models can be equations, graphs, diagrams, verbal descriptions, dioramas…the list goes on. Basically, if we're using one thing (like a "volcano" made out of Play-doh) to stand in for and represent another thing (like, y'know, a volcano), while also helping us understand the real thing better, then that's a model.

8. How does peer review help improve an experiment's scientific results?

By having a scientist's peers (that is, other scientists who know our topic of study just as well as we do) review their experiments, a lot of scientific errors can be caught. Hidden assumptions, poorly collected data, interesting follow-up questions, and confusing graphs can all be pointed out before the wider world gets a look at the experiment. Plus, the writing and presentation of results can be polished up to a mirror shine, so it'll be as plain as possible just how ground-breaking and awesome this experiment really is.

9. What are the differences between laws, hypotheses, and theories?

A law is a description or equation of some specific phenomenon. The relationship between the pressure, volume, and temperature of an ideal gas, PV = nRT, is a law. A hypothesis is a proposed explanation for how or why something occurs; it totally has to be a testable idea, though. Theories are a big deal—they are a unifying idea that gather together and explain a ton of observations and experiments. They cut through and make sense of a bunch of confusing, seemingly unrelated data. Each of these three scientific explanations are doing different things, so there's pretty much no way for one of them to turn into the others.

10. What's the relationship between science and technology?

These two fields are BFFs, but they spend their workdays in different ways. Science is all about gathering observations about the natural world, devising explanations for why things are the way they are, and then testing those ideas. Technology is all about the gadgets, though—building things that help solve people's problems. While technology uses scientific findings to create new stuff, and scientists use technology to peer into the universe's every nook and cranny, they are two separate fields of study.