The Scientific Method

Most of us learned about the scientific method maybe as early as elementary school. This was a rather simple list, such as: (1) ask a question; (2) form a hypothesis; (3) conduct an experiment; (4) record what happened and; (5) come to a conclusion. However, these five steps always aren’t always so straight forward, linear, and simple as they seem in the geosciences (or any scientific discipline for that matter). All science does start with curiosity and the development of a question.

Let’s walk through an example of the scientific method. Perhaps you like to garden (we sure do!), and you begin to wonder how the growth of your tomato plants will be affected by different types of fertilizers. To test this, you come up with a hypothesis, such as: If tomato seedlings are given fertilizer for the first two months of their growth, then they will grow to be 50% taller than tomato seedlings who did not receive fertilizer. The next step would be to buy fertilizer, tomato seeds, pots, soil, and begin your experiment. But what if your hypothesis was wrong, and the fertilized tomato seedling didn’t grow faster than the unfertilized seeds? In this case, you would have to revise your original hypothesis, and/or develop new tests to determine why the fertilizer did not affect plant growth. Often, the process of designing and conducting experiments takes up a lot of time, and may be repeated several times before results are obtained and we reach a conclusion about our experiment.

Therefore, the scientific method for scientists is long, and often repetitive. As scientist, we are very concerned with developing meaningful experiments, and obtaining results. If our hypothesis is wrong, that is totally okay! The scientific method, and science in general, is all about explaining the world around us, and sometimes an invalid hypotheses means there might be additional factors in our experiments or processes at work that we did not understand before. But, through rigorous tests and revision of hypotheses, scientists try to understand the unknown.

Hypothesis Testing

The scientific method and experimental design are not trying to ‘prove’ the truth, but rather are trying to get close to the truth through hypothesis testing.

As discussed above, a hypothesis is a statement based on observations in the natural world. How exactly do you go about formulating a hypothesis? It’s really quite simple! Making observations is the first step. These observations can either be quantitative (as in measurements or other numbers) or qualitative (as in descriptive properties). You want to think about patterns that you see in your daily life, for example: the sun rises in the east. You can test this by waking up before the sun rises, looking to the east and verifying your statement of observation. You want the data (observations you collect) to be repeatable and reliable. This means that someone else needs to be able to test the same hypothesis with your data, this will strengthen the evidence for or against your hypothesis. You also don’t want to let what you expect to happen skew your understanding of the results.

Hypotheses can either be rejected or failed to be rejected (supported). Importantly, hypotheses cannot be proven or disproven – science is about being able to test these observations with the understanding that new observations may change our understanding of the natural world.

Hypothesis vs. Theory

Often as members of our society, we come across comments and articles on social media or in the news refer to scientific breakthroughs as ‘just a theory’. In addition, you have probably heard the phrase “I have a theory!” stated by an actor on a TV show or movie. Maybe you yourself have even said this same phrase.

But what is a theory, and how does it differ from a hypothesis? Recall that a hypothesis is a statement based on observations of the natural world, and can be tested using experiments. This is different from a theory, which is defined as “a plausible or scientifically acceptable general principle or body of principles offered to explain phenomena” (Merriam-Webster dictionary definition). In other words, a theory is a formally accepted explanation for some aspect of the natural world. For example, evolution and climate change are both theories: they have been tested using several different experiments by several different researchers, all who have added information to the way in which new species form and how Earth’s climate has changed through geologic time.

Thus, the phrase ‘It’s just a theory’ is not appropriate, as theories are built from hypotheses over decades, and often centuries, worth of scientific exploration and repeated experiments. Theories are well-developed and accepted by the scientific community as reliable explanations for the natural world.

So, the next time you have an idea about the way something works or why it should work that way, instead of stating you have a theory, shout ‘I have a hypothesis’!

What are Data?

Before exploring what data can be, we should first remind everyone that data is actually plural. In any scientific discipline, scientists collect and use data to interpret the world around us. But what exactly are data, and where do they come from?

Merriam-Webster’s definition of data is as follows: “1. factual information (as measurements or statistics) used as a basis for reasoning, discussion, or calculation; 2. information output by a sensing device or organ that includes both useful and irrelevant or redundant information and must be processed to be meaningful; 3. information in numerical form that can be digitally transmitted or processed”. As geoscientists, we are mostly concerned with the first definition, where data is factual information in the form of measurements, statistics, models, etc.

But where does data come from, and how do we obtain it? This is often a broad question to answer, as different fields within geology use different methods to obtain different types of data. Therefore, throughout our site we will stick to discussing the data used in paleontology and paleoclimatology to make interpretations about evolution, the Earth’s past climate, and the future conditions of Earth.

Error in Data

If you input good or reliable data into your analysis you will find that you have results that you can properly interpret. Something that all scientists must remember is that if you input garbage data you get a garbage output. By good data we mean things such as: obtaining exact measurements, weights, etc. Because scientists are human, there is most always some amount of error within the data. HOWEVER, scientists are aware of error, and have several ways to determine and communicate error in data. It is VERY IMPORTANT in any field of science that scientists understand the amount of error, and communicate that to others in their field when that data is presented in a published paper.

There are a variety of ways to assess error in scientific work, we won’t go into extreme detail because the assessment is variable depending on the question being addressed. Author’s commonly use statistics to examine the significance of specific variables. Some use probabilistic models to get at the likelihood of a particular problem or question. Everything relates back to the the problem you are trying to address!

Precision and Accuracy

In science, the terms precision and accuracy are used often to quantify the amount of error in measurements and experiments. The two terms are different, but related. Accuracy is the closeness of a measured value to a known value. For example, if you weigh a sack of potatoes whose known weight is 1 pound (16 ounces), but the measurement on your scale reads 0.99 pounds (15.84 ounces), then your measurement is pretty accurate. If you use a second scale to weigh the same sack of potatoes and it reads 0.56 pounds (8.96 ounces), then this measurement is not very accurate at all compared to the known weight of 1 pound.

Precision is the closeness of two measured values to one another. Continuing the example from above, you weigh the  the sack of potatoes on the second scale and it reads 0.55 pounds (8.8 ounces). This is very close the first weight the second scale gave you of 0.56 pounds. Thus, the second scale is weighing with precision, but because those values deviate so much from the true weight of the sack of potatoes, these weights are not accurate.

Thus, a measurement can be precise but not accurate, and on the flip side, measurements can be accurate without precision (the values are close to the known measurement, but far from each other).

Biases in Data

Commonly, researchers experience bias, in which we unknowingly skew our data in favor of a certain outcome. If you go into an experiment certain of the results you may bias the way you think about them. These biases may not always be avoidable – particularly in geology but it is very important that as scientists we are aware of them and create and use methods that eliminate bias in our data.

Sampling Bias
This type of bias occurs in locations around the world. The fossil record has been extensively studied in North America and Europe, because this is where many of the research institutions are. Does this mean that there are not abundant fossils elsewhere? No! It means we need to write more grants to explore these areas that have been neglected and work with local institutions to further our science.

Geographic Bias
Some locations are hard to get to – would you rather sample in an area with no vegetation (plant life) or lots of vegetation? No plants often make it easier to collect fossils – do you want to go collecting in a jungle or a desert? The other bad thing about a lot of plant life is that they are often associated with water – water is incredibly erosive (it wears down rocks).

How to avoid error in science

As stated previously, there are several different types of errors in science. But scientists are VERY aware of these sources of error, and have worked very hard to greatly reduce error. This can be in the careful calibration of our instruments (such as mass spectrometers and scales) to using statistical techniques to quantify how significant our findings are. Because the topic of error and biases in science is so large, stay tuned into our ‘Science Bytes‘, ‘Meet the Scientist’, and ‘Climate & Paleo News‘ blogs to learn more about them!