Geology For Dummies. Alecia M. Spooner

as many factors as possible. For example, to test the above prediction, I will wash one white T-shirt with white laundry soap and one white T-shirt with pink laundry soap. I will wash them in the same washing machine with the same type of water so that everything (except the soap) is the same.

      5 Perform the experiment. Time to do the laundry! If my prediction is correct, the shirt washed with pink soap will turn pink.

      6 Observe the outcome. Both white T-shirts are still white after being washed with the different types of soap.

      7 Interpret and draw conclusions from the outcome of the experiment. Scientists may run a single experiment multiple times in order to get as much information as possible and make sure that they haven’t made any mistakes that could affect the outcome. After they have all this new information, they draw a conclusion. For example, in my experiment, it appears that the color of the laundry soap is not what turns white T-shirts pink in the washing machine. At this point I can propose a new hypothesis about why my T-shirts have turned pink, and I can conduct a new experiment.

      8  Share the findings with other scientists. This is possibly the most important step in the process of science. Sharing your results with other scientists provides you with new insights to your questions and conclusions. In my example, I did not confirm my hypothesis. Quite the opposite: I confirmed that the color of the laundry soap is not responsible for changing the color of my T-shirts. This is still very important information for the community of scientists trying to determine what, exactly, turns white T-shirts pink in the washing machine. Knowing my results will lead other scientists to develop and test new hypotheses and predictions.

      Next, I describe in more detail each step of the scientific method approach to answering questions.

      Sensing something new

      The first step in the scientific method is simply to use your senses. What do you see, feel, taste, smell, or hear? Each of your senses helps you collect information or observations of the world around you. Scientific observations are information collected about the physical world without manipulating it. (Manipulations come later, with experiments; keep reading for the details!)

      After you have collected multiple observations, you may find that there is a pattern — each dog you pet feels soft — or you may find that some observations are different from the others — most of the dogs have brown fur, but some have white fur with black spots. By summarizing your observations in this way, you prepare to take the next step in the scientific method, developing a hypothesis.

      I have a hypothesis!

      After you have summarized your observations, it’s time to propose an educated guess about the processes behind the patterns you observe. That educated guess is your hypothesis. In everyday speech people often say, “I have a theory” when they really mean “I have a hypothesis.” (I’ll get to theories in a few pages.)

      

A hypothesis is an inference about the patterns you have observed, based on your observations and any previous knowledge you have about the topic. It’s possible to have many different hypotheses about the observed patterns. How do you know which one is correct? You test it with an experiment, which I describe next.

      Testing your hypothesis: Experiments

      Now the real fun begins: experimenting to determine if one of your hypotheses is correct. Scientists use their hypotheses to develop predictions that can be tested. Based on the observations about the color of dog fur, a prediction could be this: “I predict that all dogs have either brown fur or white fur with black spots.” The prediction is a restatement of my hypothesis, based on my observations.

      To determine if my prediction is correct I need to collect more information. I will make new observations, but this time I will manipulate the situation and observe the outcome. In other words, this time my observations will be the result of an experiment.

      

In science, the experimental design, or the way you go about collecting the new information, is very important. An experimental design describes the parameters of your experiment: how many samples you will take (how many observations you will make) and how you will choose those samples. These decisions are partly determined by the question you are asking and partly determined by the nature of the observations you are collecting.

      In most cases it’s impossible to observe every single instance of the physical world that you are exploring. Therefore, you must take a sample that can represent the rest. For example, I can’t look at every dog in the world to see what color their fur is, so I may decide that looking at 100 dogs will provide me with enough observations to determine if my prediction is correct. Those 100 are a sample of the worldwide population of dogs. If I choose those 100 dogs wisely, they may be a very accurate representation of the worldwide dog population. The best sample size is different for each experiment; it all depends on the question being asked.

In earth science, experiments are often natural or observational. This means that scientists go out into the field and observe events that have already happened, such as the formation of rocks, rock layers, or features of the landscape. Scientists make these observations without changing any aspect of the event or its result.

      Geologists also use another kind of experiment called a manipulative experiment. A manipulative experiment is done in a laboratory, where the scientist can manipulate or change certain factors in order to test which factors are most important in creating the observed outcome. In this case, multiple experiments can be done, each one testing the importance of a different factor (or variable), with the goal of zeroing in on the one (or ones) that explain the observed outcome.

      

Most importantly, a scientific experiment, whether it is a natural or manipulated experiment, must be repeatable. This means that the scientists must clearly describe the steps they have taken so that another scientist can repeat the same experiment and see if she too, gets the same result.

      Crunching the numbers

      After running experiments and making observations, a scientist is left with a large collection of information, or data, to use to draw a conclusion. Trying to find patterns in page after page of descriptive observations or lists of numbers is almost impossible. To find patterns in the data, a scientist uses statistics.

      

Statistics are a mathematical tool for describing and comparing information (observations) quantitatively, which simply means using numbers. By using numbers to describe the data, such as the number of times a certain characteristic is observed in different rock samples, scientists can organize and compare the patterns in the data using simple arithmetic.

      Some people find statistics intimidating because they seem like complicated mathematical formulas. But really, statistical methods are simple mathematics combined in a step-by-step sequence to uncover patterns in the data. Some statistics determine if two sets of data have overall similarities or differences. Others determine which variables are most important in creating the observed outcomes.

Another reason scientists organize and describe their data quantitatively is so that they can display it using graphs. Many different types of graphs are used, and a scientist must determine which type of graph best displays the data in an understandable way. The most suitable graph depends on what type of data is being displayed. Figure 2-1 illustrates a few common graph types used in earth science:

       Pie