Lesson Three: The Method of Science

All right, now you have a problem and you have developed the expertise you need to do the work. Fair enough, you have to proceed scientifically to solve the problem. How does a scientist solve problems? That is what we hope to answer here.

Everything begins and ends with nature, or at least it should do so in any true science. What makes something scientific is not simply showing that an argument is logically self-consistent, it is in showing that the argument is in complete agreement with nature. How do you do this? By experimentation.

We have been throwing a lot of words around, it is time to settle down into the meat of the lesson.

Task-List:

  1. Write a definition of the word nature.
  2. List a few examples of things that are in nature. For each example explain why you included it.
  3. List a few examples of things that are not in nature. Explain why they are not.
  4. All scientists are keen observers of nature. All of their work stems from their observations of nature. It doesn't matter if those observations come from watching animals in the wild, stars in the heavens, an experimental apparatus, a line on a graph, or a set of equations. All of science begins with questions about nature. You yourself have already asked such a question of nature. Now it is time to write an operational definition of the word observation. Give three examples of observations, and explain why you included each. Give three counterexamples of observations with explanations of each.
  5. Think about how your project question is based on an observation.
  6. Think about the role of technology in making observations. Our senses are extremely fine-tuned, but the sensors given to us by technology extend the ability of our senses to almost unimaginable limits. Write an operational definition for the word sensor. Give three examples of sensors and why you included each. Give three counterexamples of sensors and why they are not sensors.
  7. The use of sensors allows us a unique capability to assign numbers to the properties of our environment. One offshoot of this ability is the requirement of developing a uniform system of assigning numbers to the properties of our environment. The process of assigning such a number is called measurement. A uniform system for making measurements is called a standard unit of measure. Give three examples of standard units of measure, what they measure, and why you include them. Give three examples of units that are not standard, what they measure, and why they are not standards.
  8. Information gained from observations, with or without measurement, is called data. Data from measurements is called numerical or quantitative data and is probably the most useful data. Data gained without measurement is called qualitative data and is not quite so useful. The important thing to remember about data of any kind is that it must be recorded as soon as it is collected. Write your observations in your project notebook. When you make measurements, write them down right away. Modern sensors can be attached to a computer and the data can be written to a computer file. Note the name of such a file (and its location) in your notebook. Always make disk/tape/CD backups of all data.
  9. Once we have collected some data we can study it. What do we look for when we study data? We look for patterns in the data. Specifically, we look for patterns relating to our question. When we think we have found a pattern in the data we try to quantify that pattern as best we can. There are numerous books written about data analysis and we will not delve too deeply into it right now. An important part of data analysis is the need to define and account for errors in the data, without such an error analysis numerical data is meaningless. Based upon your results from lesson 2 can you list three patterns drawn from data relating to your project? It is vital for experimenters to have a strong grasp of the concepts of data analysis. Many theorists work to develop new techniques of data analysis. In many ways, data analysis is the backbone of modern science.
  10. There are now several avenues of investigation open to you. You can attempt to predict the ramifications of any possible pattern you have found, this is the heart of theoretical science. You can attempt to refine your measurements, or find other examples of your pattern in nature, this is the heart of observational science. You can attempt to recreate the pattern in a laboratory, this is the heart of experimental science. Or you can attempt to recreate the pattern on a computer, this is the heart of computational science.
  11. If you make a prediction you must prove that it is logically self-consistent. The most efficient way to do this is to convert the pattern/predicition under investigation into a mathematical statement, and then use the principles of logic to mathematically prove the idea true. This is not an easy thing to do! If you succeed at that then you have a model. You must show that the model is actually true in nature. This requires observation or experimentation.

You will always be collecting new data and looking for new patterns. In this way the process of science never ends.

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