Nature of Science

Think of your favorite science fiction movie. What is it about? Maybe it’s about spaceships going to distant planets, or people being cloned in laboratories, or undersea civilizations, or robots that walk among us. These entertaining imaginings are make-believe fantasies, that’s why they’re called science “fiction.” They are not real. But why are they called “science” fiction?

The answer is that science uses a disciplined process to answer questions. In science, "disciplined" does not mean well-behaved. It means following orderly steps in order to come up with the best answers. Science involves observing, wondering, categorizing, communicating, calculating, analyzing, and much more. In order to convert creativity into reality, we need science. In order to travel beyond where anyone has gone before, we need science. In order to understand the world, make sense of it, and conserve it, we need science. In order to confirm our best guesses about the universe and the things in it, we need science. Science fiction stories extend and expand on all the ideas of science and technology in creative ways.
Lesson Objectives
Explain the importance of asking questions.
State the steps of the scientific method.
Describe the three major types of scientific models.
Use appropriate safety precautions inside and outside the science laboratory.
Asking Questions
Why is the sky blue?

How tall will this tree grow?

Why does the wind blow so hard?

Will it be cold tonight?

How many stars are out there?

Are there planets like Earth that orbit about some of those stars?

How did this rock get holes in it?

Why are some rocks sharp and jagged, while others are round?

You probably ask yourself a thousand questions a day, many of which you never ask anyone else. For many of the questions you do ask, you never even get an answer. But your brain keeps churning with questions and curiosity. We can't help but want to know.

The list of questions above are some of the same questions that scientists ask. Science has developed over centuries and centuries, and our ability to measure the tiniest trait has increased immensely. So although there is no wrong question, there are questions that lend themselves more to the scientific process than others. In other words, some questions can be investigated using the scientific method while others rely on pure faith or opinion.
Scientific Methods

The scientific method is not a list of instructions but a series of steps that help to investigate a question. By using the scientific method, we can have greater confidence in how we evaluate that question. Sometimes, the order of the steps in the scientific method can change, because more questions arise from observations or data that we collect. The basic sequence followed in the scientific method is illustrated in Figure 1.1.

Question
The scientific method almost always begins with a question that helps to focus the investigation. What are we studying? What do we want to know? What is the problem we want to solve? The best questions for scientific investigation are specific as opposed to general; they imply what factors may be observed or manipulated.

Example: A farmer has heard of a farming method called “no-till farming.” In this method, certain techniques in planting and fertilizing eliminate the need for tilling (or plowing) the land. Will no-till farming reduce the erosion of the farmland (
![200px-Erosion.jpg](https://steemitimages.com/DQmNRUoeNUv4iy9YiZakjKgcFgThCvftLE52C3PupYct4gk/200px-
Erosion.jpg)
Research
Before we go any further, it is important to find out what is already known about the topic. You can research a topic by looking up books and magazines in the library, searching on the Internet, and even talking to people who are experts in the area. By learning about your topic, you’ll be able to make thoughtful predictions. Your experimental design might be influenced by what you have researched. Or you might even find that your question has been researched thoroughly. Although repeating experiments is valid and important in science, you may choose to introduce new ideas into your investigation, or you may change your initial question.

Example: The farmer decides to research the topic of no-till farming (Figure 1.3). She finds sources on the Internet, at the library, and at the local farming supply store that discuss what type of fertilizer might be used and what the best spacing for her crop would be. She even finds out that no-till farming can be a way to reduce carbon dioxide emissions into the atmosphere, which helps in the fight against global warming.

Hypothesis
Now that you have researched the topic, you can make an educated guess or explanation to the question. This is your hypothesis. The best hypothesis is directly related to the question and is testable, so that you can do experiments to determine whether your hypothesis is correct.

Example: The farmer has researched her question and developed the following hypothesis:

No-till farming will decrease the soil loss on hills of similar steepness as compared to the traditional farming technique because there will be less disturbance to the soil.

A hypothesis can be either proved or disproved by testing. If a hypothesis is repeatedly tested and proven to be true, then scientists will no longer call it a hypothesis. A scientific theory has a great deal of supporting evidence that backs it up.

Experiment
Not all questions can be tested by experimentation. However, many questions present us with ways to test them that give us the clearest conclusions. When we design experiments, we select the factor that will be manipulated or changed. This is the independent variable. We will also choose all of the factors that must remain the same. These are the experimental controls. Finally, we will choose the factor that we are measuring, as we change the independent variable. This is the dependent variable. We might say that the dependent variable “depends” on the independent variable. How much soil is eroded depends on the type of farming technique that we choose.

Example: The farmer will conduct an experiment on two separate hills with similar slopes or steepnesses (Figure 1.4). On one hill, he will use a traditional farming technique which includes plowing to stir up the nutrients in the soil. On the other hill, he will use a no-till technique by spacing plants further apart and using specialized equipment that plants the plants without tilling. He will give both sets of plants identical amounts of water and fertilizer.
![500px-Cropscientist.jpg(https://steemitimages.com/DQmRgSAcWzHJKCGdCghD4Ww3whjxgZU3r9vt3hLEEaqD5er/500px-
Cropscientist.jpg)
In this case, the independent variable is the farming technique—either traditional or no-till—because that is what is being manipulated. In order to be able to compare the two hills, they must have the same slope and the same amount of fertilizer and water. If one had a different slope, then it could be the angle that affects the erosion, not the farming technique, for example. These are the controls. Finally, the dependent variable is the amount of erosion because the farmer will measure the erosion to analyze its relatedness to the farming technique.

Data and Experimental Error
Data can be collected in many different ways depending on what we are interested in finding out. Scientists use electron microscopes to explore the universe of tiny objects and telescopes to venture into the universe itself. Scientists routinely travel to the bottom of the ocean in research submersibles to make observations and collect samples. Probes are used to make observations in places that are too dangerous or too impractical for scientists to venture. Probes have explored the Titanic as it lay on the bottom of the ocean and to other planets in our solar system. Data from the probes travels through cables or through space to a computer where it can be manipulated by scientists. Of course, many scientists work in a laboratory and perform experiments and analyses on a bench top.

During an experiment, we may make many measurements. These measurements are our observations that will be carefully recorded in an organized manner. This data is often computerized and kept in a spreadsheet that can be in the form of charts or tables that are clearly labeled, so that we won’t forget what each number represents. "Data" refers to the list of measurements that we have collected. We may make written descriptions of our observations but often, the most useful data is numerical. Even data that is difficult to measure with a number is sometimes represented numerically. For example, we may make observations about cleanliness on a scale from one to ten, where ten is very clean and one is very dirty. Statistical analyses also allow us to make more effective use of the data by allowing us to show relationships between different categories of data. Statistics can make sense of the variability (spread) in a data set. By graphing data, we can visually understand the relationships between data. Besides graphs, data can be displayed as charts or drawings so that other people who are interested can see the relationships easily.

As in just about every human endeavor, errors are unavoidable. In an experiment, systematic errors are inherent in the experimental setup so that the numbers are always skewed in one direction or another. For example, a scale may always measure one-half ounce high. Like many systematic errors, the scale can be recalibrated or the error can be easily corrected. Random errors occur because no measurement can be made exactly precisely. For example, a stopwatch may be stopped too soon or too late. This type of error is reduced if many measurements are taken and then averaged. Sometimes a result is inconsistent with the results from other samples. If enough tests have been done, the inconsistent data point can be thrown out since likely a mistake was made in that experiment. The remaining results can be averaged.

Not all data is quantified, however. Our written descriptions are qualitative data, data that describes the situation observed. In any case, data is used to help us draw logical conclusions.

Conclusions
After you have summarized the results of the experiments and presented the data as graphs, tables and diagrams, you can try to draw a conclusion from the experiments. You must gather all your evidence and background information. Then using logic you need to try to formulate an explanation for your data. What is the answer to the question based on the results of the experiment? A conclusion should include comments about the hypothesis. Was the hypothesis supported or not? Some experiments have clear, undeniable results that completely support the hypothesis. Others do not support the hypothesis. However, all experiments contribute to our wealth of knowledge. Even experiments that do not support the hypothesis may teach us new information that we can learn from. In the world of science, hypotheses are rarely proved to one hundred percent certainty. More often than not, experiments lead to even more questions and more possible ways of considering the same idea.

Example: After a full year of running her experiment, the farmer finds 2.2 times as much erosion on the traditionally farmed hill as on the no-till hill. She intends to use no-till methods of farming from now on and to continue researching other factors that may affect erosion. The farmer also notices that plants in the no-till plots are taller and the soil moisture seems higher. She decides to repeat the experiment and measure soil moisture, plant growth, and total water needed to irrigate in each kind of farming.

Theory
If a topic is of interest to scientists, many scientists will conduct experiments and make observations, which they will publish in scientific journals. Over time the evidence will mount in, for, or against the hypothesis being tested. If a hypothesis explains all the data and no data contradicts the hypothesis, the hypothesis becomes a theory. A theory is supported by many observations and there are no major inconsistencies. A theory is also used to predict behavior. Although a theory can be overthrown if conflicting data is discovered, the longer a theory has been in existence the more data it probably has to back it up and the less likely it will be proven wrong. A theory is a model of reality that is simpler than the phenomenon itself.

The common usage of the word theory is very different from the scientific usage; e.g. I have a theory as to why Joe likes Sue more than Rae. The word hypothesis would be more correct in most cases.

Scientific Models
Many scientists use models to understand and explain ideas. Models are representations of objects or systems. Models are often very useful because they are more practical and simpler than the real life object. They may be manipulated and adjusted more easily. There are three types of models and each type is useful in certain ways. Each has drawbacks as well.

Physical Models
Physical models are physical representations of whatever subject is being studied. These models may be simplified by leaving out certain real components, but will contain the important elements. Model cars and toy dinosaurs are examples of physical models. Drawings and maps are also physical models. They allow us to see and feel and move them, so that we can compare them to one another and illustrate certain features.

We can use a drawing to model the layers of the Earth (Figure 1.5). This type of model is useful in understanding the composition of the Earth, the relative temperatures within the Earth, and the changing densities of the Earth beneath the surface. Yet there are many differences between a cut-away model of the Earth and the real thing. First of all, the size is much different. It is difficult to understand the size of the Earth by looking at a simple drawing. You can’t get a good idea of the movement of substances beneath the surface by looking at a drawing that does not move. The model is very useful but has its shortcomings.

Conceptual Models

Figure 1.6: A collision showing a meteor striking the Earth.
A conceptual model is not a physical model, but rather a mental explanation that ties together many ideas to attempt to explain something. A conceptual model tries to combine knowledge and must incorporate new knowledge that may change it as knowledge is acquired. The origin of the moon, for example, is explained by some as a Mars sized planet that hit the Earth and formed a great cloud of debris and gas (Figure 1.6). This debris and gas eventually formed a single spherical body called the Moon. This is a useful model of an event that probably occurred billions of years ago. It incorporates many ideas about the craters and volcanoes on the Moon, and the similarity of some elements on both the moon and the Earth. Not all data may fit this model, however, and there may be much information that we simply don’t know. Some people think that the Moon was initially an asteroid out in space which was captured in orbit by the gravity of the Earth. This may be a competing conceptual model which has its own arguments and weaknesses. As with physical models, all conceptual models have limitations.

Mathematical Models
A third type of model is the mathematical model. These models are created through a great deal of consideration and analysis of data. A mathematical model is an equation or formula that takes many factors or variables into account. These models may help predict complex events like tornadoes and climate change. In order to predict climate change, for example, a mathematical model may take into account factors such as temperature readings, ice density, snow fall, and humidity. These data may be plugged into equations to give a prediction. As with other models, not all factors can be accounted for, so that the mathematical model may not work perfectly. This may yield false alarms or prediction failures. No model is without its limitations.

Models are a useful tool in science. They allow us to efficiently demonstrate ideas and create hypotheses. They give us visual or conceptual manners for thinking about things. They allow us to make predictions and conduct experiments without all of the difficulties of real-life objects. Could you imagine trying to explain a plant cell by only using a real plant cell or trying to predict the next alignment of planets by only looking at them? In general, models have limitations that should be taken into consideration before any prediction is believed or any conclusion seen as fact.